mirror of
https://github.com/chanzuckerberg/cellxgene.git
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Charlotte Weaver
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[bumpversion]
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current_version = 0.4.0
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current_version = 0.0.2
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[bumpversion:file:setup.py]
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search = version="{current_version}"
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@@ -1,5 +0,0 @@
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bin
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client
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dist
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docs
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server
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@@ -11,11 +11,10 @@ install:
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- ./bin/build-client
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- pip install -e .
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- pip install -r server/requirements-dev.txt
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- docker build .
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script:
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- set -eo pipefail
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- flake8 server
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- black --check
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- flake8 server/app/
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- flake8 server/cli/
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- npm run --prefix client/ build
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- npm run --prefix client/ test
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- pytest -s server/test
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FROM ubuntu:bionic
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ENV LC_ALL=C.UTF-8
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ENV LANG=C.UTF-8
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RUN apt-get update && \
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apt-get install -y build-essential libxml2-dev python3-dev python3-pip zlib1g-dev && \
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pip3 install cellxgene
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ENTRYPOINT ["cellxgene"]
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recursive-include server/app/web/templates *
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recursive-include server/app/web/static *
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include server/requirements.txt
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@@ -1,294 +1,111 @@
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# cellxgene
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> an interactive explorer for single-cell transcriptomics data
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### An interactive, performant explorer for single cell transcriptomics data.
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`cellxgene` is an interactive data explorer for single-cell transcriptomics datasets, such as those coming from the [Human Cell Atlas](https://humancellatlas.org). Leveraging modern web development techniques to enable fast visualizations of at least 1 million cells, we hope to enable biologists and computational researchers to explore their data, and to demonstrate general, scalable, and reusable patterns for scientific data visualization.
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<img align="right" width="350" height="218" src="./example-dataset/cellxgene-demo.gif" pad="50px">
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cellxgene is an open-source experiment in how to bring powerful tools from modern web development to visualize and explore large single-cell transcriptomics datasets.
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Started in the context of the Human Cell Atlas Consortium, cellxgene hopes to both enable scientists to explore their data and to equip developers with scalable, reusable patterns and frameworks for visualizing large scientific datasets.
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|
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<img src="https://raw.githubusercontent.com/chanzuckerberg/cellxgene/master/docs/cellxgene-demo-1.gif" width="200" height="200" hspace="30"><img src="https://raw.githubusercontent.com/chanzuckerberg/cellxgene/master/docs/cellxgene-demo-2.gif" width="200" height="200" hspace="30"><img src="https://raw.githubusercontent.com/chanzuckerberg/cellxgene/master/docs/cellxgene-demo-3.gif" width="200" height="200" hspace="30">
|
||||
## Features
|
||||
|
||||
## getting started
|
||||
- **Visualization at scale:** built with [WebGL](https://www.khronos.org/webgl/), [React](https://reactjs.org/) & [Redux](https://redux.js.org/) to handle visualization of at least 1 million cells.
|
||||
|
||||
You'll need **python 3.6** and **Google Chrome**. (*Warning*: Python 3.7 is **not** supported at this time)
|
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The web UI is tested on OSX and Windows using Chrome, and the python CLI is tested on OSX and Ubuntu (via WSL/Windows). It should work on other platforms, but if you run into trouble let us know (see [help](#help-and-contact) below).
|
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- **Interactive exploration:** select, cross-filter, and compare subsets of your data with performant indexing and data handling.
|
||||
|
||||
- **Flexible API:** the cellxgene client-server model is designed to support a range of existing analysis packages for backend computational tasks (eg scanpy), integrated with client-side visualization via a [REST API](https://restfulapi.net/).
|
||||
|
||||
To install run
|
||||
## Getting Started
|
||||
|
||||
```
|
||||
pip install cellxgene
|
||||
```
|
||||
|
||||
To start exploring a dataset call
|
||||
|
||||
```
|
||||
cellxgene launch dataset.h5ad --open
|
||||
```
|
||||
|
||||
If you want an example dataset download [this file](https://github.com/chanzuckerberg/cellxgene/raw/master/example-dataset/pbmc3k.h5ad) and then call
|
||||
|
||||
```
|
||||
cellxgene launch pbmc3k.h5ad --open
|
||||
```
|
||||
|
||||
You should see your web browser open with the following
|
||||
|
||||
<img width="450" src="https://raw.githubusercontent.com/chanzuckerberg/cellxgene/master/docs/cellxgene-opening-screenshot.png" pad="50px">
|
||||
|
||||
**Note**: automatic opening of the browser with the `--open` flag only works on OS X, on other platforms you'll need to directly point to the provided link in your browser.
|
||||
|
||||
There are several options available, such as:
|
||||
|
||||
- `--layout` to specify the layout as `tsne` or `umap`
|
||||
- `--title` to show a title on the explorer
|
||||
- `--open` to automatically open the web browser after launching (OS X only)
|
||||
|
||||
To see all options call
|
||||
|
||||
```
|
||||
cellxgene launch --help
|
||||
```
|
||||
|
||||
There is an additional subcommand called `cellxgene prepare` that takes an existing dataset in one of several formats and applies minimal preprocessing and reformatting so that `launch` can use it (see [the next section](##data-formatting) for more info on `prepare`).
|
||||
|
||||
## data formatting
|
||||
|
||||
### assumptions
|
||||
|
||||
The `launch` command assumes that the data is stored in the `.h5ad` format from the [`anndata`](https://anndata.readthedocs.io/en/latest/index.html) library. It also assumes that certain computations have already been performed. Briefly, the `.h5ad` format wraps a two-dimensional `ndarray` and stores additional metadata as "annotations" for either observations (referred to as `obs` and `obsm`) or variables (`var` and `varm`). `cellxgene launch` makes the following assumptions about your data (we recommend loading and inspecting your data using `scanpy` to validate these assumptions)
|
||||
|
||||
- an `obs` field has a unique identifier for every cell (you can specify which field to use with the `--obs-names` option, by default it will use the value of `data.obs_names`)
|
||||
- a `var` field has a unique identifier for every gene (you can specify which field to use with the `--var-names` option, by default it will use the value of `data.var_names`)
|
||||
- an `obsm` field contains the two-dimensional coordinates for the layout that you want to render (e.g. `X_tsne` for the `tsne` layout or `X_umap` for the `umap` layout)
|
||||
- any additional `obs` fields will be rendered as per-cell continuous or categorical metadata by the app (e.g. `louvain` cluster assignments)
|
||||
|
||||
### prepare
|
||||
|
||||
The `prepare` command is included to help you format your data. It uses `scanpy` under the hood. This is especially useful if you are starting with raw unanalyzed data and are unfamiliar with `scanpy`.
|
||||
|
||||
To prepare from an existing `.h5ad` file use
|
||||
|
||||
```
|
||||
cellxgene prepare dataset.h5ad --output=dataset-processed.h5ad
|
||||
```
|
||||
|
||||
This will load the input data, perform PCA and nearest neighbor calculations, compute `umap` and `tsne` layouts and `louvain` cluster assignments, and save the results in a new file called `dataset-processed.h5ad` that can be loaded using `cellxgene launch`. Data can be loaded from several formats, including `.h5ad` `.loom` and a `10-Genomics-formatted` `mtx` directory. Several options are available, including running one of the preprocessing `recipes` included with `scanpy`, which include steps like cell filtering and gene selection. To learn more about the `recipes` please see the `scanpy` [documentation](https://github.com/theislab/scanpy/blob/master/scanpy/preprocessing/recipes.py).
|
||||
|
||||
Depending on the options chosen, `prepare` can take a long time to run (a few minutes for datasets with 10-100k cells, up to an hour or more for datasets with >100k cells). If you want `prepare` to run faster we recommend using the `sparse` option and only computing the layout for `umap`, using a call like this
|
||||
|
||||
```
|
||||
cellxgene prepare dataset.h5ad --output=dataset-processed.h5ad --layout=umap --sparse
|
||||
```
|
||||
|
||||
To see all options call
|
||||
|
||||
```
|
||||
cellxgene prepare --help
|
||||
```
|
||||
|
||||
**Note**: `cellxgene prepare` will only perform `louvain` clustering if you have the `python-igraph` and `louvain` packages installed. To make sure they are installed alongside `cellxgene` use
|
||||
|
||||
```
|
||||
pip install cellxgene[louvain]
|
||||
```
|
||||
|
||||
If the aforementioned optional package installation fails, you can also install these packages directly:
|
||||
|
||||
```
|
||||
pip install python-igraph louvain>=0.6
|
||||
```
|
||||
|
||||
## conda and virtual environments
|
||||
|
||||
If you use conda and want to create a conda environment for `cellxgene` you can use the following commands
|
||||
|
||||
```
|
||||
conda create --yes -n cellxgene python=3.6
|
||||
conda activate cellxgene
|
||||
pip install cellxgene
|
||||
```
|
||||
|
||||
Or you can create a virtual environment by using
|
||||
|
||||
```
|
||||
ENV_NAME=cellxgene
|
||||
python3.6 -m venv ${ENV_NAME}
|
||||
source ${ENV_NAME}/bin/activate
|
||||
pip install cellxgene
|
||||
```
|
||||
|
||||
## docker
|
||||
|
||||
We have included a dockerfile to conveniently run cellxgene from docker.
|
||||
|
||||
1. Build the image `docker build . -t cellxgene`
|
||||
2. Run the container and mount data `docker run -v "$PWD/example-dataset/:/data/" -p 5005:5005 cellxgene launch --host 0.0.0.0 data/pbmc3k.h5ad`
|
||||
* You will need to use --host 0.0.0.0 to have the container listen to incoming requests from the browser
|
||||
|
||||
|
||||
## FAQ
|
||||
|
||||
<details>
|
||||
|
||||
<summary> questions about data formatting </summary>
|
||||
|
||||
<hr>
|
||||
|
||||
> Someone sent me a directory of `10X-Genomics` data with a `mtx` file and I've never used `scanpy`, can I use `cellxgene`?
|
||||
|
||||
Yep! This should only take a couple steps. We'll assume your data is in a folder called `data/` and you've successfully installed `cellxgene` with the `louvain` packages as described above. Just run
|
||||
|
||||
```
|
||||
cellxgene prepare data/ --output=data-processed.h5ad --layout=umap
|
||||
```
|
||||
|
||||
Depending on the size of the dataset, this may take some time. Once it's done, call
|
||||
|
||||
```
|
||||
cellxgene launch data-processed.h5ad --layout=umap --open
|
||||
```
|
||||
|
||||
And your web browser should open with an interactive view of your data.
|
||||
|
||||
<hr>
|
||||
|
||||
> In my `prepare` command I received the following error `Warning: louvain module is not installed, no clusters will be calculated. To fix this please install cellxgene with the optional feature louvain enabled`
|
||||
|
||||
Louvain clustering requires additional dependencies that are somewhat complex, so we don't include them by default. For now, you need to specify that you want these packages by using
|
||||
|
||||
```
|
||||
pip install cellxgene[louvain]
|
||||
```
|
||||
|
||||
<hr>
|
||||
|
||||
> I ran `prepare` and I'm getting results that look unexpected
|
||||
|
||||
You might want to try running one of the preprocessing recipes included with `scanpy` (read more about them [here](https://scanpy.readthedocs.io/en/latest/api/index.html#recipes)). You can specify this with the `--recipe` option, such as
|
||||
|
||||
```
|
||||
cellxgene prepare data/ --output=data-processed.h5ad --recipe=zheng17
|
||||
```
|
||||
|
||||
It should be easy to run `prepare` then call `cellxgene launch` a few times with different settings to explore different behaviors. We may explore adding other preprocessing options in the future.
|
||||
|
||||
<hr>
|
||||
|
||||
> I have extra metadata that I want to add to my dataset
|
||||
|
||||
Currently this is not supported directly, but you should be able to do this manually using `scanpy`. For example, this [notebook](https://github.com/falexwolf/fun-analyses/blob/master/tabula_muris/tabula_muris.ipynb) shows adding the contents of a `csv` file with metadata to an `anndata` object. For now, you could do this manually on your data in the same way and then save out the result before loading into `cellxgene`.
|
||||
|
||||
<hr>
|
||||
|
||||
> What part of the anndata objects does cellxgene pull in for visualization?
|
||||
|
||||
- `.obs` and `.var` annotations are use to extract metadata for filtering
|
||||
- `.X` is used to display expression (histograms, scatterplot & colorscale) and to compute differential expression
|
||||
- `.obsm` is used for layout
|
||||
|
||||
<hr>
|
||||
|
||||
> When I start cellxgene, I get an error `Unexpected HTTP response 500, INTERNAL SERVER ERROR -- Out of range float values are not JSON compliant` in the web UI, or `Warning: JSON encoding failure - suggest trying --nan-to-num command line option` in the CLI. What can I do?
|
||||
|
||||
At the moment, cellxgene is unable to transmit floating point NaN or Inifinty values to the web UI (due to a limitation on data serialization method in use). We expect to resolve this in a future release, but in the meantime, you can work around this issue by starting cellxgene with the `--nan-to-num` command line option, ie, `cellxgene launch data.h5ad --nan-to-num`.
|
||||
|
||||
This option will convert all NaNs to zero, and all positive/negative infinities to the min/max of the data element within which the value was found (eg, +Infinity within an `obs` annotation will be converted to the maximum finite value in that annotation). This option will increase startup time, so we recommend only using it when the dataset contains NaN/Infinities.
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary> questions about installing and building </summary>
|
||||
|
||||
<hr>
|
||||
|
||||
> I tried to `pip install cellxgene` and got a weird error I don't understand
|
||||
|
||||
This may happen, especially as we work out bugs in our installation process! Please create a new [Github issue](https://github.com/chanzuckerberg/cellxgene/issues), explain what you did, and include all the error messages you saw. It'd also be super helpful if you call `pip freeze` and include the full output alongside your issue.
|
||||
|
||||
<hr>
|
||||
|
||||
> I'm following the developer instructions and get an error about "missing files and directories” when trying to build the client
|
||||
|
||||
This is likely because you do not have node and npm installed, we recommend using [nvm](https://github.com/creationix/nvm) if you're new to using these tools.
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary> questions about algorithms </summary>
|
||||
|
||||
<hr>
|
||||
|
||||
> How are you computing and sorting differential expression results?
|
||||
|
||||
Currently we use a [Welch's _t_-test](https://en.wikipedia.org/wiki/Welch%27s_t-test) implementation including the same variance overestimation correction as used in `scanpy`. We sort the `tscore` to identify the top N genes, and then filter to remove any that fall below a cutoff log fold change value, which can help remove spurious test results. The default threshold is `0.01` and can be changed using the option `--diffexp-lfc-cutoff`. We can explore adding support for other test types in the future.
|
||||
|
||||
</details>
|
||||
|
||||
## developer guide
|
||||
|
||||
This project has made a few key design choices
|
||||
|
||||
- The front-end is built with [`regl`](https://github.com/regl-project/regl) (a webgl library), [`react`](https://reactjs.org/), [`redux`](https://redux.js.org/), [`d3`](https://github.com/d3/d3), and [`blueprint`](https://blueprintjs.com/docs/#core) to handle rendering large numbers of cells with lots of complex interactivity
|
||||
- The app is designed with a client-server model that can support a range of existing analysis packages for backend computational tasks (currently built for [scanpy](https://github.com/theislab/scanpy))
|
||||
- The client uses fast cross-filtering to handle selections and comparisons across subsets of data
|
||||
|
||||
Depending on your background and interests, you might want to contribute to the frontend, or backend, or both!
|
||||
|
||||
If you are interested in working on `cellxgene` development, we recommend cloning the project from Gitub. First you'll need the following installed on your machine
|
||||
**Requirements**
|
||||
|
||||
- OS: OSX, Windows, Linux -- the developers are currently testing on OSX and Windows (via WSL using Ubuntu). It should work on other platforms but if you are using something different and need help, please let us know.
|
||||
- python 3.6
|
||||
- node and npm (we recommend using [nvm](https://github.com/creationix/nvm) if this is your first time with node)
|
||||
- python3 tkinter
|
||||
- npm
|
||||
- Google Chrome
|
||||
|
||||
Then clone the project
|
||||
**Clone project**
|
||||
|
||||
```
|
||||
git clone https://github.com/chanzuckerberg/cellxgene.git
|
||||
```
|
||||
git clone https://github.com/chanzuckerberg/cellxgene.git
|
||||
|
||||
Build the client web assets by calling this from inside the `cellxgene` folder
|
||||
**Install client**
|
||||
|
||||
```
|
||||
./bin/build-client
|
||||
```
|
||||
cd cellxgene
|
||||
./bin/build-client
|
||||
|
||||
Install all requirements (we recommend doing this inside a virtual environment)
|
||||
**To use with virtual env for python**
|
||||
(optional, but recommended)
|
||||
|
||||
```
|
||||
pip install -e .
|
||||
```
|
||||
ENV_NAME=cellxgene
|
||||
python3 -m venv ${ENV_NAME}
|
||||
source ${ENV_NAME}/bin/activate
|
||||
|
||||
You can start the app while developing either by calling `cellxgene` or by calling `python -m server`. We recommend using the `--debug` flag to see more output, which you can include when reporting bugs.
|
||||
**Install server**
|
||||
|
||||
If you have any questions about developing or contributing, come hang out with us by joining the [CZI Science Slack](https://join-cellxgene-users.herokuapp.com/) and posting in the `#cellxgene-dev` channel.
|
||||
pip install -e .
|
||||
|
||||
## development roadmap
|
||||
**Run (with demo data)**
|
||||
|
||||
`cellxgene` is still very much in development, and we've love to include the community as we plan new features to work on. We are thinking about working on the following features over the next 3-12 months. If you are interested in updates, want to give feedback, want to contribute, or have ideas about other features we should work on, please [contact us](#help-and-contact)
|
||||
cellxgene launch --title PBMC3K example-dataset/pbmc3k.h5ad
|
||||
|
||||
- **Visualizaling spatial metadata** Image-based transcriptomics methods also generate large cell by gene matrices, alongside rich metadata about spatial location; we would like to render this information in `cellxgene`
|
||||
- **Visualizing trajectories** Trajectory analyses infer progression along some ordering or pseudotime; we would like `cellxgene` to render the results of these analyses when they have been performed
|
||||
- **Deploy to web** Many projects release public data browser websites alongside their publicatons; we would like to make it easy for anyone to deploy `cellxgene` to a custom URL with their own dataset that they own and operate
|
||||
- **HCA Integration** The [Human Cell Atlas](https://humancellatlas.org) is generating a large corpus of single-cell expression data and will make it available through the Data Coordination Platform; we would like `cellxgene` to be one of several different portals for browsing these data
|
||||
**Help**
|
||||
|
||||
## contributing
|
||||
cellxgene --help
|
||||
|
||||
We warmly welcome contributions from the community! Please submit any bug reports and feature requests through [Github issues](https://github.com/chanzuckerberg/cellxgene/issues). Please submit any direct contributions by forking the repository, creating a branch, and submitting a Pull Request. It'd be great for PRs to include test cases and documentation updates where relevant, though we know the core test suite is itself still a work in progress. And all code contributions and dependencies must be compatible with the project's open-source license (MIT). If you have any questions about this stuff, just ask!
|
||||
_For help with the scanpy engine_
|
||||
|
||||
## inspiration and collaboration
|
||||
cellxgene scanpy --help
|
||||
|
||||
We've been heavily inspired by several other related single-cell visualization projects, including the [UCSC Cell Browswer](http://cells.ucsc.edu/), [Cytoscape](http://www.cytoscape.org/), [Xena](https://xena.ucsc.edu/), [ASAP](https://asap.epfl.ch/), [Gene Pattern](http://genepattern-notebook.org/), and many others. We hope to explore collaborations where useful as this community works together on improving interactive visualization for single-cell data.
|
||||
## Using your own data
|
||||
|
||||
We were inspired by Mike Bostock and the [crossfilter](https://github.com/crossfilter) team for the design of our filtering implementation.
|
||||
### Scanpy
|
||||
|
||||
We have been working closely with the [`scanpy`](https://github.com/theislab/scanpy) team to integrate with their awesome analysis tools. Special thanks to Alex Wolf, Fabian Theis, and the rest of the team for their help during development and for providing an example dataset.
|
||||
To prepare your data you will need to format your data into AnnData format using scanpy and calculate PCA and nearest neighbors and save in h5ad format.
|
||||
|
||||
We are eager to explore integrations with other computational backends such as [`Seurat`](https://github.com/satijalab/seurat) or [`Bioconductor`](https://github.com/Bioconductor)
|
||||
1. [Load data into scanpy](https://scanpy.readthedocs.io/en/latest/api/index.html#reading)
|
||||
|
||||
## help and contact
|
||||
- Ensure that `obs`'s index is the cell names: `print(data.obs_names)` should show your cell indices. If it shows gene names, you may need to just call `data.transpose()`.
|
||||
|
||||
Have questions, suggestions, or comments? You can come hang out with us by joining the [CZI Science Slack](https://join-cellxgene-users.herokuapp.com/) and posting in the `#cellxgene-users` channel. As mentioned above, please submit any feature requests or bugs as [Github issues](https://github.com/chanzuckerberg/cellxgene/issues). We'd love to hear from you!
|
||||
2. Calculate PCA
|
||||
|
||||
## reuse
|
||||
sc.pp.pca(data) ## sc is scanpy.api
|
||||
|
||||
This project was started with the sole goal of empowering the scientific community to explore and understand their data. As such, we encourage other scientific tool builders in academia or industry to adopt the patterns, tools, and code from this project, and reach out to us with ideas or questions. All code is freely available for reuse under the [MIT license](https://opensource.org/licenses/MIT).
|
||||
3. Calculate nearest neighbors (depending on layout algorithm)
|
||||
|
||||
```
|
||||
# For umap layout algorithm, you need to use the "umap" method for neighbors
|
||||
sc.pp.neighbors(data, method="umap", metric="euclidean", use_rep="X_pca")
|
||||
|
||||
# For tsne layout algorithm, you can use either "umap" or "gauss"; we recommend "gauss"
|
||||
sc.pp.neighbors(data, method="gauss", metric="euclidean", use_rep="X_pca")
|
||||
```
|
||||
|
||||
4. Save file
|
||||
|
||||
```
|
||||
# cellxgene requires file to be named data.h5ad
|
||||
data.write("data.h5ad")
|
||||
```
|
||||
|
||||
## Contributing
|
||||
|
||||
We warmly welcome contributions from the community. Please submit any bug reports and feature requests through github issues. Please submit any direct contributions via a branch + pull request.
|
||||
|
||||
## Inspiration and collaboration
|
||||
|
||||
We’ve been inspired by several other related efforts in this space, including the [UCSC Cell Browswer](http://cells.ucsc.edu/), [Cytoscape](http://www.cytoscape.org/), [Xena](https://xena.ucsc.edu/), [ASAP](https://asap.epfl.ch/), [Gene Pattern](http://genepattern-notebook.org/), & many others; we hope to explore collaborations where useful.
|
||||
|
||||
## Help/Contact
|
||||
|
||||
Have questions, suggestions, or comments? You can contact us by joining [CZI Science Slack](https://cziscience.slack.com/messages/CCTA8DF1T) and posting in the #cellxgene channel. Please submit any feature requests or bugs as an issue in github. We'd love to hear from you!
|
||||
|
||||
## Reuse
|
||||
|
||||
This project was started with the sole goal of empowering the scientific community to explore and understand their data. As such, we whole-heartedly encourage other scientific tool builders to adopt the patterns, tools, and code from this project, and reach out to us with ideas or questions using Github Issues or Pull Requests. All code is freely available for reuse under the [MIT license](https://opensource.org/licenses/MIT).
|
||||
|
||||
## Acknowledgements
|
||||
|
||||
cellxgene is inspired by many innovative projects. We would like to specifically thank:
|
||||
|
||||
- Alex Wolf for the demo dataset.
|
||||
- Mike Bostock and the [crossfilter](https://github.com/crossfilter) team for API inspiration.
|
||||
|
||||
@@ -7,8 +7,6 @@ echo "removing node_modules"
|
||||
rm -rf $CELLXGENE_DIR/client/node_modules
|
||||
echo "removing client_build"
|
||||
rm -rf $CELLXGENE_DIR/client/build
|
||||
echo "removing dist"
|
||||
rm -rf $CELLXGENE_DIR/dist
|
||||
echo "removing egg-info"
|
||||
rm -rf $CELLXGENE_DIR/cellxgene.egg-info
|
||||
echo "removing static files"
|
||||
|
||||
@@ -1,192 +0,0 @@
|
||||
import summarizeAnnotations from "../../../src/util/stateManager/summarizeAnnotations";
|
||||
|
||||
describe("summarizeAnnotations", () => {
|
||||
const schema = {
|
||||
annotations: {
|
||||
obs: [
|
||||
{ name: "name", type: "string" },
|
||||
{ name: "nameString", type: "string" },
|
||||
{ name: "nameBoolean", type: "boolean" },
|
||||
{ name: "nameFloat32", type: "float32" },
|
||||
{ name: "nameInt32", type: "int32" },
|
||||
{
|
||||
name: "nameCategorical",
|
||||
type: "categorical",
|
||||
categories: [true, false, 1, 0, 0.00001, 4383.4833, "test", "", "0"]
|
||||
}
|
||||
],
|
||||
var: [{ name: "name", type: "string" }]
|
||||
}
|
||||
};
|
||||
|
||||
test("empty test", () => {
|
||||
const summary = summarizeAnnotations(schema, [], []);
|
||||
expect(summary).toEqual(
|
||||
expect.objectContaining({
|
||||
obs: {
|
||||
nameString: {
|
||||
categorical: true,
|
||||
categories: [],
|
||||
categoryCounts: new Map(),
|
||||
numCategories: 0
|
||||
},
|
||||
nameBoolean: {
|
||||
categorical: true,
|
||||
categories: [],
|
||||
categoryCounts: new Map(),
|
||||
numCategories: 0
|
||||
},
|
||||
nameFloat32: {
|
||||
categorical: false,
|
||||
range: {
|
||||
max: Number.NEGATIVE_INFINITY,
|
||||
min: Number.POSITIVE_INFINITY
|
||||
}
|
||||
},
|
||||
nameInt32: {
|
||||
categorical: false,
|
||||
range: {
|
||||
max: Number.NEGATIVE_INFINITY,
|
||||
min: Number.POSITIVE_INFINITY
|
||||
}
|
||||
},
|
||||
nameCategorical: {
|
||||
categorical: true,
|
||||
categories: [],
|
||||
categoryCounts: new Map(),
|
||||
numCategories: 0
|
||||
}
|
||||
},
|
||||
var: {}
|
||||
})
|
||||
);
|
||||
});
|
||||
|
||||
test("simple test", () => {
|
||||
const obsAnnotations = [
|
||||
{
|
||||
__index__: 0,
|
||||
name: "n1",
|
||||
nameString: "hi",
|
||||
nameBoolean: true,
|
||||
nameFloat32: 39.3,
|
||||
nameInt32: 99,
|
||||
nameCategorical: 1
|
||||
}
|
||||
];
|
||||
const varAnnotations = [];
|
||||
|
||||
const summary = summarizeAnnotations(
|
||||
schema,
|
||||
obsAnnotations,
|
||||
varAnnotations
|
||||
);
|
||||
|
||||
expect(summary).toEqual(
|
||||
expect.objectContaining({
|
||||
obs: {
|
||||
nameString: {
|
||||
categorical: true,
|
||||
categories: ["hi"],
|
||||
categoryCounts: new Map([["hi", 1]]),
|
||||
numCategories: 1
|
||||
},
|
||||
nameBoolean: {
|
||||
categorical: true,
|
||||
categories: [true],
|
||||
categoryCounts: new Map([[true, 1]]),
|
||||
numCategories: 1
|
||||
},
|
||||
nameFloat32: {
|
||||
categorical: false,
|
||||
range: { min: 39.3, max: 39.3 }
|
||||
},
|
||||
nameInt32: {
|
||||
categorical: false,
|
||||
range: { min: 99, max: 99 }
|
||||
},
|
||||
nameCategorical: {
|
||||
categorical: true,
|
||||
categories: [1],
|
||||
categoryCounts: new Map([[1, 1]]),
|
||||
numCategories: 1
|
||||
}
|
||||
},
|
||||
var: {}
|
||||
})
|
||||
);
|
||||
});
|
||||
|
||||
test("multi test", () => {
|
||||
const obsAnnotations = [
|
||||
{
|
||||
__index__: 0,
|
||||
name: "n0",
|
||||
nameString: "hi",
|
||||
nameBoolean: false,
|
||||
nameFloat32: 39.3,
|
||||
nameInt32: 99,
|
||||
nameCategorical: 1
|
||||
},
|
||||
{
|
||||
__index__: 1,
|
||||
name: "n1",
|
||||
nameString: "hi",
|
||||
nameBoolean: true,
|
||||
nameFloat32: 39.3,
|
||||
nameInt32: 99,
|
||||
nameCategorical: false
|
||||
},
|
||||
{
|
||||
__index__: 2,
|
||||
name: "n2",
|
||||
nameString: "bye",
|
||||
nameBoolean: true,
|
||||
nameFloat32: 0,
|
||||
nameInt32: 99,
|
||||
nameCategorical: "0"
|
||||
}
|
||||
];
|
||||
const varAnnotations = [];
|
||||
|
||||
const summary = summarizeAnnotations(
|
||||
schema,
|
||||
obsAnnotations,
|
||||
varAnnotations
|
||||
);
|
||||
|
||||
expect(summary).toMatchObject(
|
||||
expect.objectContaining({
|
||||
obs: {
|
||||
nameString: {
|
||||
categorical: true,
|
||||
categories: expect.arrayContaining(["hi", "bye"]),
|
||||
categoryCounts: new Map([["hi", 2], ["bye", 1]]),
|
||||
numCategories: 2
|
||||
},
|
||||
nameBoolean: {
|
||||
categorical: true,
|
||||
categories: expect.arrayContaining([true, false]),
|
||||
categoryCounts: new Map([[true, 2], [false, 1]]),
|
||||
numCategories: 2
|
||||
},
|
||||
nameFloat32: {
|
||||
categorical: false,
|
||||
range: { min: 0, max: 39.3 }
|
||||
},
|
||||
nameInt32: {
|
||||
categorical: false,
|
||||
range: { min: 99, max: 99 }
|
||||
},
|
||||
nameCategorical: {
|
||||
categorical: true,
|
||||
categories: expect.arrayContaining([1, false, "0"]),
|
||||
categoryCounts: new Map([[1, 1], [false, 1], ["0", 1]]),
|
||||
numCategories: 3
|
||||
}
|
||||
},
|
||||
var: {}
|
||||
})
|
||||
);
|
||||
});
|
||||
});
|
||||
@@ -1,45 +0,0 @@
|
||||
import {
|
||||
countCategoryValues2D,
|
||||
clearCaches
|
||||
} from "../../../src/util/stateManager/worldUtil";
|
||||
|
||||
describe("WorldUtil cache management", () => {
|
||||
test("empty", () => {
|
||||
const count = countCategoryValues2D("a", "b", []);
|
||||
expect(count).toMatchObject(new Map());
|
||||
});
|
||||
|
||||
test("simple couts", () => {
|
||||
const rows = [{ a: 0, b: false }, { a: 0, b: true }, { a: 1, b: false }];
|
||||
const count = countCategoryValues2D("a", "b", rows);
|
||||
expect(count).toMatchObject(
|
||||
new Map([
|
||||
[0, new Map([[true, 1], [false, 1]])],
|
||||
[1, new Map([[false, 1]])]
|
||||
])
|
||||
);
|
||||
});
|
||||
|
||||
test("memo cache clear", () => {
|
||||
clearCaches();
|
||||
const row1 = [];
|
||||
const row2 = [{ a: 0, b: false }, { a: 0, b: true }, { a: 1, b: false }];
|
||||
const count1 = countCategoryValues2D("a", "b", row1);
|
||||
const count2 = countCategoryValues2D("a", "b", row1);
|
||||
const count3 = countCategoryValues2D("a", "b", []);
|
||||
const count4 = countCategoryValues2D("a", "b", row2);
|
||||
|
||||
clearCaches();
|
||||
const count10 = countCategoryValues2D("a", "b", row1);
|
||||
const count11 = countCategoryValues2D("a", "b", row2);
|
||||
|
||||
expect(count1).toEqual(count2);
|
||||
expect(count1).toEqual(count3);
|
||||
expect(count1).toEqual(count10);
|
||||
expect(count1).not.toBe(count3);
|
||||
expect(count1).not.toBe(count10);
|
||||
|
||||
expect(count4).toEqual(count11);
|
||||
expect(count4).not.toBe(count11);
|
||||
});
|
||||
});
|
||||
@@ -34,8 +34,7 @@ module.exports = {
|
||||
"object-curly-newline": ["error", { consistent: true }],
|
||||
"react/prop-types": [0],
|
||||
"space-before-function-paren": "off",
|
||||
"function-paren-newline": "off",
|
||||
"prefer-destructuring": ["error", { object: true, array: false }]
|
||||
"function-paren-newline": "off"
|
||||
},
|
||||
overrides: [
|
||||
{
|
||||
|
||||
Generated
+36
-48
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "cellxgene",
|
||||
"version": "0.4.0",
|
||||
"version": "0.0.2",
|
||||
"lockfileVersion": 1,
|
||||
"requires": true,
|
||||
"dependencies": {
|
||||
@@ -2194,7 +2194,7 @@
|
||||
},
|
||||
"babel-plugin-syntax-object-rest-spread": {
|
||||
"version": "6.13.0",
|
||||
"resolved": "https://registry.npmjs.org/babel-plugin-syntax-object-rest-spread/-/babel-plugin-syntax-object-rest-spread-6.13.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/babel-plugin-syntax-object-rest-spread/-/babel-plugin-syntax-object-rest-spread-6.13.0.tgz",
|
||||
"integrity": "sha1-/WU28rzhODb/o6VFjEkDpZe7O/U=",
|
||||
"dev": true
|
||||
},
|
||||
@@ -2627,7 +2627,7 @@
|
||||
},
|
||||
"browserify-aes": {
|
||||
"version": "1.2.0",
|
||||
"resolved": "https://registry.npmjs.org/browserify-aes/-/browserify-aes-1.2.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/browserify-aes/-/browserify-aes-1.2.0.tgz",
|
||||
"integrity": "sha512-+7CHXqGuspUn/Sl5aO7Ea0xWGAtETPXNSAjHo48JfLdPWcMng33Xe4znFvQweqc/uzk5zSOI3H52CYnjCfb5hA==",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -2664,7 +2664,7 @@
|
||||
},
|
||||
"browserify-rsa": {
|
||||
"version": "4.0.1",
|
||||
"resolved": "https://registry.npmjs.org/browserify-rsa/-/browserify-rsa-4.0.1.tgz",
|
||||
"resolved": "http://registry.npmjs.org/browserify-rsa/-/browserify-rsa-4.0.1.tgz",
|
||||
"integrity": "sha1-IeCr+vbyApzy+vsTNWenAdQTVSQ=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -2718,7 +2718,7 @@
|
||||
},
|
||||
"buffer": {
|
||||
"version": "4.9.1",
|
||||
"resolved": "https://registry.npmjs.org/buffer/-/buffer-4.9.1.tgz",
|
||||
"resolved": "http://registry.npmjs.org/buffer/-/buffer-4.9.1.tgz",
|
||||
"integrity": "sha1-bRu2AbB6TvztlwlBMgkwJ8lbwpg=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -3394,7 +3394,7 @@
|
||||
},
|
||||
"create-hash": {
|
||||
"version": "1.2.0",
|
||||
"resolved": "https://registry.npmjs.org/create-hash/-/create-hash-1.2.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/create-hash/-/create-hash-1.2.0.tgz",
|
||||
"integrity": "sha512-z00bCGNHDG8mHAkP7CtT1qVu+bFQUPjYq/4Iv3C3kWjTFV10zIjfSoeqXo9Asws8gwSHDGj/hl2u4OGIjapeCg==",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -3407,7 +3407,7 @@
|
||||
},
|
||||
"create-hmac": {
|
||||
"version": "1.1.7",
|
||||
"resolved": "https://registry.npmjs.org/create-hmac/-/create-hmac-1.1.7.tgz",
|
||||
"resolved": "http://registry.npmjs.org/create-hmac/-/create-hmac-1.1.7.tgz",
|
||||
"integrity": "sha512-MJG9liiZ+ogc4TzUwuvbER1JRdgvUFSB5+VR/g5h82fGaIRWMWddtKBHi7/sVhfjQZ6SehlyhvQYrcYkaUIpLg==",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -4090,7 +4090,7 @@
|
||||
},
|
||||
"diffie-hellman": {
|
||||
"version": "5.0.3",
|
||||
"resolved": "https://registry.npmjs.org/diffie-hellman/-/diffie-hellman-5.0.3.tgz",
|
||||
"resolved": "http://registry.npmjs.org/diffie-hellman/-/diffie-hellman-5.0.3.tgz",
|
||||
"integrity": "sha512-kqag/Nl+f3GwyK25fhUMYj81BUOrZ9IuJsjIcDE5icNM9FJHAVm3VcUDxdLPoQtTuUylWm6ZIknYJwwaPxsUzg==",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -4705,7 +4705,7 @@
|
||||
},
|
||||
"load-json-file": {
|
||||
"version": "2.0.0",
|
||||
"resolved": "https://registry.npmjs.org/load-json-file/-/load-json-file-2.0.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/load-json-file/-/load-json-file-2.0.0.tgz",
|
||||
"integrity": "sha1-eUfkIUmvgNaWy/eXvKq8/h/inKg=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -4891,7 +4891,7 @@
|
||||
},
|
||||
"events": {
|
||||
"version": "1.1.1",
|
||||
"resolved": "https://registry.npmjs.org/events/-/events-1.1.1.tgz",
|
||||
"resolved": "http://registry.npmjs.org/events/-/events-1.1.1.tgz",
|
||||
"integrity": "sha1-nr23Y1rQmccNzEwqH1AEKI6L2SQ="
|
||||
},
|
||||
"evp_bytestokey": {
|
||||
@@ -5202,22 +5202,11 @@
|
||||
"randomatic": "^3.0.0",
|
||||
"repeat-element": "^1.1.2",
|
||||
"repeat-string": "^1.5.2"
|
||||
},
|
||||
"dependencies": {
|
||||
"is-number": {
|
||||
"version": "2.1.0",
|
||||
"resolved": "https://registry.npmjs.org/is-number/-/is-number-2.1.0.tgz",
|
||||
"integrity": "sha1-Afy7s5NGOlSPL0ZszhbezknbkI8=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
"kind-of": "^3.0.2"
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"finalhandler": {
|
||||
"version": "1.1.1",
|
||||
"resolved": "https://registry.npmjs.org/finalhandler/-/finalhandler-1.1.1.tgz",
|
||||
"resolved": "http://registry.npmjs.org/finalhandler/-/finalhandler-1.1.1.tgz",
|
||||
"integrity": "sha512-Y1GUDo39ez4aHAw7MysnUD5JzYX+WaIj8I57kO3aEPT1fFRL4sr7mjei97FgnwhAyyzRYmQZaTHb2+9uZ1dPtg==",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -5933,11 +5922,6 @@
|
||||
"integrity": "sha1-GwqzvVU7Kg1jmdKcDj6gslIHgyc=",
|
||||
"dev": true
|
||||
},
|
||||
"fuzzysort": {
|
||||
"version": "1.1.4",
|
||||
"resolved": "https://registry.npmjs.org/fuzzysort/-/fuzzysort-1.1.4.tgz",
|
||||
"integrity": "sha512-JzK/lHjVZ6joAg3OnCjylwYXYVjRiwTY6Yb25LvfpJHK8bjisfnZJ5bY8aVWwTwCXgxPNgLAtmHL+Hs5q1ddLQ=="
|
||||
},
|
||||
"get-caller-file": {
|
||||
"version": "1.0.3",
|
||||
"resolved": "https://registry.npmjs.org/get-caller-file/-/get-caller-file-1.0.3.tgz",
|
||||
@@ -5952,7 +5936,7 @@
|
||||
},
|
||||
"get-stream": {
|
||||
"version": "3.0.0",
|
||||
"resolved": "https://registry.npmjs.org/get-stream/-/get-stream-3.0.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/get-stream/-/get-stream-3.0.0.tgz",
|
||||
"integrity": "sha1-jpQ9E1jcN1VQVOy+LtsFqhdO3hQ=",
|
||||
"dev": true
|
||||
},
|
||||
@@ -6339,7 +6323,7 @@
|
||||
},
|
||||
"html-webpack-plugin": {
|
||||
"version": "3.2.0",
|
||||
"resolved": "https://registry.npmjs.org/html-webpack-plugin/-/html-webpack-plugin-3.2.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/html-webpack-plugin/-/html-webpack-plugin-3.2.0.tgz",
|
||||
"integrity": "sha1-sBq71yOsqqeze2r0SS69oD2d03s=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -6415,7 +6399,7 @@
|
||||
},
|
||||
"http-errors": {
|
||||
"version": "1.6.3",
|
||||
"resolved": "https://registry.npmjs.org/http-errors/-/http-errors-1.6.3.tgz",
|
||||
"resolved": "http://registry.npmjs.org/http-errors/-/http-errors-1.6.3.tgz",
|
||||
"integrity": "sha1-i1VoC7S+KDoLW/TqLjhYC+HZMg0=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -6638,7 +6622,7 @@
|
||||
},
|
||||
"is-builtin-module": {
|
||||
"version": "1.0.0",
|
||||
"resolved": "https://registry.npmjs.org/is-builtin-module/-/is-builtin-module-1.0.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/is-builtin-module/-/is-builtin-module-1.0.0.tgz",
|
||||
"integrity": "sha1-VAVy0096wxGfj3bDDLwbHgN6/74=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -6768,9 +6752,13 @@
|
||||
"dev": true
|
||||
},
|
||||
"is-number": {
|
||||
"version": "7.0.0",
|
||||
"resolved": "https://registry.npmjs.org/is-number/-/is-number-7.0.0.tgz",
|
||||
"integrity": "sha512-41Cifkg6e8TylSpdtTpeLVMqvSBEVzTttHvERD741+pnZ8ANv0004MRL43QKPDlK9cGvNp6NZWZUBlbGXYxxng=="
|
||||
"version": "2.1.0",
|
||||
"resolved": "https://registry.npmjs.org/is-number/-/is-number-2.1.0.tgz",
|
||||
"integrity": "sha1-Afy7s5NGOlSPL0ZszhbezknbkI8=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
"kind-of": "^3.0.2"
|
||||
}
|
||||
},
|
||||
"is-obj": {
|
||||
"version": "1.0.1",
|
||||
@@ -7641,7 +7629,7 @@
|
||||
},
|
||||
"json5": {
|
||||
"version": "0.5.1",
|
||||
"resolved": "https://registry.npmjs.org/json5/-/json5-0.5.1.tgz",
|
||||
"resolved": "http://registry.npmjs.org/json5/-/json5-0.5.1.tgz",
|
||||
"integrity": "sha1-Hq3nrMASA0rYTiOWdn6tn6VJWCE=",
|
||||
"dev": true
|
||||
},
|
||||
@@ -7731,7 +7719,7 @@
|
||||
},
|
||||
"load-json-file": {
|
||||
"version": "1.1.0",
|
||||
"resolved": "https://registry.npmjs.org/load-json-file/-/load-json-file-1.1.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/load-json-file/-/load-json-file-1.1.0.tgz",
|
||||
"integrity": "sha1-lWkFcI1YtLq0wiYbBPWfMcmTdMA=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -8055,7 +8043,7 @@
|
||||
"dependencies": {
|
||||
"minimist": {
|
||||
"version": "1.2.0",
|
||||
"resolved": "https://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
|
||||
"integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=",
|
||||
"dev": true
|
||||
}
|
||||
@@ -8180,7 +8168,7 @@
|
||||
},
|
||||
"minimist": {
|
||||
"version": "0.0.8",
|
||||
"resolved": "https://registry.npmjs.org/minimist/-/minimist-0.0.8.tgz",
|
||||
"resolved": "http://registry.npmjs.org/minimist/-/minimist-0.0.8.tgz",
|
||||
"integrity": "sha1-hX/Kv8M5fSYluCKCYuhqp6ARsF0=",
|
||||
"dev": true
|
||||
},
|
||||
@@ -8225,7 +8213,7 @@
|
||||
},
|
||||
"mkdirp": {
|
||||
"version": "0.5.1",
|
||||
"resolved": "https://registry.npmjs.org/mkdirp/-/mkdirp-0.5.1.tgz",
|
||||
"resolved": "http://registry.npmjs.org/mkdirp/-/mkdirp-0.5.1.tgz",
|
||||
"integrity": "sha1-MAV0OOrGz3+MR2fzhkjWaX11yQM=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -9951,7 +9939,7 @@
|
||||
},
|
||||
"parse-asn1": {
|
||||
"version": "5.1.1",
|
||||
"resolved": "https://registry.npmjs.org/parse-asn1/-/parse-asn1-5.1.1.tgz",
|
||||
"resolved": "http://registry.npmjs.org/parse-asn1/-/parse-asn1-5.1.1.tgz",
|
||||
"integrity": "sha512-KPx7flKXg775zZpnp9SxJlz00gTd4BmJ2yJufSc44gMCRrRQ7NSzAcSJQfifuOLgW6bEi+ftrALtsgALeB2Adw==",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -10659,7 +10647,7 @@
|
||||
},
|
||||
"readable-stream": {
|
||||
"version": "2.3.6",
|
||||
"resolved": "https://registry.npmjs.org/readable-stream/-/readable-stream-2.3.6.tgz",
|
||||
"resolved": "http://registry.npmjs.org/readable-stream/-/readable-stream-2.3.6.tgz",
|
||||
"integrity": "sha512-tQtKA9WIAhBF3+VLAseyMqZeBjW0AHJoxOtYqSUZNJxauErmLbVm2FW1y+J/YA9dUrAC39ITejlZWhVIwawkKw==",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -11681,7 +11669,7 @@
|
||||
},
|
||||
"minimist": {
|
||||
"version": "1.2.0",
|
||||
"resolved": "https://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
|
||||
"integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=",
|
||||
"dev": true
|
||||
},
|
||||
@@ -11868,7 +11856,7 @@
|
||||
},
|
||||
"sha.js": {
|
||||
"version": "2.4.11",
|
||||
"resolved": "https://registry.npmjs.org/sha.js/-/sha.js-2.4.11.tgz",
|
||||
"resolved": "http://registry.npmjs.org/sha.js/-/sha.js-2.4.11.tgz",
|
||||
"integrity": "sha512-QMEp5B7cftE7APOjk5Y6xgrbWu+WkLVQwk8JNjZ8nKRciZaByEW6MubieAiToS7+dwvrjGhH8jRXz3MVd0AYqQ==",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -12322,7 +12310,7 @@
|
||||
},
|
||||
"strip-ansi": {
|
||||
"version": "3.0.1",
|
||||
"resolved": "https://registry.npmjs.org/strip-ansi/-/strip-ansi-3.0.1.tgz",
|
||||
"resolved": "http://registry.npmjs.org/strip-ansi/-/strip-ansi-3.0.1.tgz",
|
||||
"integrity": "sha1-ajhfuIU9lS1f8F0Oiq+UJ43GPc8=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -12558,7 +12546,7 @@
|
||||
},
|
||||
"through": {
|
||||
"version": "2.3.8",
|
||||
"resolved": "https://registry.npmjs.org/through/-/through-2.3.8.tgz",
|
||||
"resolved": "http://registry.npmjs.org/through/-/through-2.3.8.tgz",
|
||||
"integrity": "sha1-DdTJ/6q8NXlgsbckEV1+Doai4fU=",
|
||||
"dev": true
|
||||
},
|
||||
@@ -13214,7 +13202,7 @@
|
||||
"dependencies": {
|
||||
"minimist": {
|
||||
"version": "1.2.0",
|
||||
"resolved": "https://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/minimist/-/minimist-1.2.0.tgz",
|
||||
"integrity": "sha1-o1AIsg9BOD7sH7kU9M1d95omQoQ=",
|
||||
"dev": true
|
||||
}
|
||||
@@ -13877,7 +13865,7 @@
|
||||
},
|
||||
"wrap-ansi": {
|
||||
"version": "2.1.0",
|
||||
"resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-2.1.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/wrap-ansi/-/wrap-ansi-2.1.0.tgz",
|
||||
"integrity": "sha1-2Pw9KE3QV5T+hJc8rs3Rz4JP3YU=",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
@@ -13985,7 +13973,7 @@
|
||||
},
|
||||
"yargs": {
|
||||
"version": "11.1.0",
|
||||
"resolved": "https://registry.npmjs.org/yargs/-/yargs-11.1.0.tgz",
|
||||
"resolved": "http://registry.npmjs.org/yargs/-/yargs-11.1.0.tgz",
|
||||
"integrity": "sha512-NwW69J42EsCSanF8kyn5upxvjp5ds+t3+udGBeTbFnERA+lF541DDpMawzo4z6W/QrzNM18D+BPMiOBibnFV5A==",
|
||||
"dev": true,
|
||||
"requires": {
|
||||
|
||||
+1
-3
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "cellxgene",
|
||||
"version": "0.4.0",
|
||||
"version": "0.0.2",
|
||||
"license": "MIT",
|
||||
"description": "cellxgene is a web application for the interactive exploration of single cell sequence data.",
|
||||
"repository": "https://github.com/chanzuckerberg/cellxgene",
|
||||
@@ -34,10 +34,8 @@
|
||||
"d3": "^4.10.0",
|
||||
"d3-scale-chromatic": "^1.3.0",
|
||||
"font-color-contrast": "^1.0.3",
|
||||
"fuzzysort": "^1.1.4",
|
||||
"gl-mat4": "^1.1.4",
|
||||
"gl-matrix": "^2.7.1",
|
||||
"is-number": "^7.0.0",
|
||||
"key-pressed": "0.0.1",
|
||||
"lodash": "^4.17.4",
|
||||
"memoize-one": "^4.0.0",
|
||||
|
||||
@@ -24,7 +24,7 @@ const doInitialDataLoad = () =>
|
||||
"config",
|
||||
"schema",
|
||||
"annotations/obs",
|
||||
"annotations/var?annotation-name=name",
|
||||
"annotations/var",
|
||||
"layout/obs"
|
||||
])
|
||||
.map(r => `${globals.API.prefix}${globals.API.version}${r}`)
|
||||
@@ -90,16 +90,12 @@ async function _doRequestExpressionData(dispatch, getState, genes) {
|
||||
const state = getState();
|
||||
const { universe } = state.controls;
|
||||
/* preload data already in cache */
|
||||
let expressionData = _.transform(
|
||||
genes,
|
||||
(expData, g) => {
|
||||
const data = kvCache.get(universe.varDataCache, g);
|
||||
if (data) {
|
||||
expData[g] = data;
|
||||
}
|
||||
},
|
||||
{}
|
||||
); // --> { gene: data }
|
||||
let expressionData = _.transform(genes, (expData, g) => {
|
||||
const data = kvCache.get(universe.varDataCache, g);
|
||||
if (data) {
|
||||
expData[g] = data;
|
||||
}
|
||||
}); // --> { gene: data }
|
||||
/* make a list of genes for which we do not have data */
|
||||
const genesToFetch = _.filter(genes, g => expressionData[g] === undefined);
|
||||
|
||||
@@ -123,6 +119,7 @@ async function _doRequestExpressionData(dispatch, getState, genes) {
|
||||
}),
|
||||
headers: new Headers({
|
||||
accept: "application/json",
|
||||
"Accept-Encoding": "gzip, deflate, br",
|
||||
"Content-Type": "application/json"
|
||||
})
|
||||
}
|
||||
@@ -242,6 +239,7 @@ const requestDifferentialExpression = (set1, set2, num_genes = 10) => async (
|
||||
method: "POST",
|
||||
headers: new Headers({
|
||||
Accept: "application/json",
|
||||
"Accept-Encoding": "gzip, deflate, br",
|
||||
"Content-Type": "application/json"
|
||||
}),
|
||||
body: JSON.stringify({
|
||||
@@ -299,9 +297,6 @@ const resetInterface = () => (dispatch, getState) => {
|
||||
type: "reset World to eq Universe",
|
||||
universe
|
||||
});
|
||||
dispatch({
|
||||
type: "increment graph render counter"
|
||||
});
|
||||
};
|
||||
|
||||
export default {
|
||||
|
||||
@@ -11,8 +11,7 @@ import actions from "../actions";
|
||||
|
||||
@connect(state => ({
|
||||
loading: state.controls.loading,
|
||||
error: state.controls.error,
|
||||
graphRenderCounter: state.controls.graphRenderCounter
|
||||
error: state.controls.error
|
||||
}))
|
||||
class App extends React.Component {
|
||||
constructor(props) {
|
||||
@@ -55,7 +54,7 @@ class App extends React.Component {
|
||||
}
|
||||
|
||||
render() {
|
||||
const { loading, error, graphRenderCounter } = this.props;
|
||||
const { loading } = this.props;
|
||||
return (
|
||||
<Container>
|
||||
<Helmet title="cellxgene" />
|
||||
@@ -80,8 +79,10 @@ class App extends React.Component {
|
||||
marginLeft: 350 /* but responsive */
|
||||
}}
|
||||
>
|
||||
{loading ? null : <Graph key={graphRenderCounter} />}
|
||||
{loading ? null : <Graph />}
|
||||
|
||||
<Legend />
|
||||
{}
|
||||
</div>
|
||||
</div>
|
||||
</Container>
|
||||
|
||||
@@ -34,7 +34,7 @@ class HistogramBrush extends React.Component {
|
||||
.scaleLinear()
|
||||
.range([this.height - this.marginBottom, 0]);
|
||||
|
||||
if (obsAnnotations[0][field] !== undefined) {
|
||||
if (obsAnnotations[0][field]) {
|
||||
// recalculate expensive stuff
|
||||
const allValuesForContinuousFieldAsArray = _.map(obsAnnotations, field);
|
||||
|
||||
@@ -215,12 +215,7 @@ class HistogramBrush extends React.Component {
|
||||
d3.select(svgRef)
|
||||
.append("g")
|
||||
.attr("class", "brush")
|
||||
.call(
|
||||
d3
|
||||
.brushX()
|
||||
.on("brush", this.onBrush(field, x.invert).bind(this))
|
||||
.on("end", this.onBrush(field, x.invert).bind(this))
|
||||
);
|
||||
.call(d3.brushX().on("end", this.onBrush(field, x.invert).bind(this)));
|
||||
|
||||
/* AXIS */
|
||||
d3.select(svgRef)
|
||||
@@ -248,9 +243,9 @@ class HistogramBrush extends React.Component {
|
||||
colorAccessor,
|
||||
isUserDefined,
|
||||
isDiffExp,
|
||||
logFoldChange,
|
||||
pval,
|
||||
pvalAdj,
|
||||
avgDiff,
|
||||
set1AvgExp,
|
||||
set2AvgExp,
|
||||
scatterplotXXaccessor,
|
||||
scatterplotYYaccessor,
|
||||
zebra
|
||||
@@ -337,17 +332,25 @@ class HistogramBrush extends React.Component {
|
||||
}}
|
||||
>
|
||||
<span>
|
||||
<strong>log fold change:</strong>
|
||||
{` ${logFoldChange.toPrecision(4)}`}
|
||||
<strong>1:</strong>
|
||||
{` ${set1AvgExp.toPrecision(2)}`}
|
||||
</span>
|
||||
<span
|
||||
style={{
|
||||
marginLeft: 7,
|
||||
backgroundColor: globals.lighterGrey,
|
||||
padding: 2
|
||||
}}
|
||||
>
|
||||
<strong>p-value (adj):</strong>
|
||||
{pvalAdj < 0.0001 ? " < 0.0001" : ` ${pvalAdj.toFixed(4)}`}
|
||||
<strong>2:</strong>
|
||||
{` ${set2AvgExp.toPrecision(2)}`}
|
||||
</span>
|
||||
<span
|
||||
style={{
|
||||
marginLeft: 7
|
||||
}}
|
||||
>
|
||||
{`Av. Diff: ${avgDiff.toFixed(2)}`}
|
||||
</span>
|
||||
</div>
|
||||
) : null}
|
||||
|
||||
@@ -5,13 +5,34 @@ import { connect } from "react-redux";
|
||||
import * as globals from "../../globals";
|
||||
import Category from "./category";
|
||||
|
||||
/* Cap the max number of displayed categories */
|
||||
const truncateCategories = options => {
|
||||
const numOptions = _.size(options);
|
||||
if (numOptions <= globals.maxCategoricalOptionsToDisplay) {
|
||||
return options;
|
||||
}
|
||||
return _(options)
|
||||
.map((v, k) => ({ name: k, val: v }))
|
||||
.sortBy("val")
|
||||
.slice(numOptions - globals.maxCategoricalOptionsToDisplay)
|
||||
.transform((r, v) => {
|
||||
r[v.name] = v.val;
|
||||
}, {})
|
||||
.value();
|
||||
};
|
||||
|
||||
@connect(state => ({
|
||||
categoricalSelectionState: state.controls.categoricalSelectionState
|
||||
ranges: _.get(state.controls.world, "summary.obs", null),
|
||||
categorySelectionLimit: _.get(
|
||||
state.config,
|
||||
"parameters.max-category-items",
|
||||
globals.configDefaults.parameters["max-category-items"]
|
||||
)
|
||||
}))
|
||||
class Categories extends React.Component {
|
||||
render() {
|
||||
const { categoricalSelectionState } = this.props;
|
||||
if (!categoricalSelectionState) return null;
|
||||
const { ranges, categorySelectionLimit } = this.props;
|
||||
if (!ranges) return null;
|
||||
|
||||
return (
|
||||
<div
|
||||
@@ -26,9 +47,27 @@ class Categories extends React.Component {
|
||||
>
|
||||
Categorical Metadata
|
||||
</p>
|
||||
{_.map(categoricalSelectionState, (catState, catName) => (
|
||||
<Category key={catName} metadataField={catName} />
|
||||
))}
|
||||
{_.map(ranges, (value, key) => {
|
||||
const isColorField = key.includes("color") || key.includes("Color");
|
||||
const isSelectableCategory =
|
||||
value.options &&
|
||||
!isColorField &&
|
||||
key !== "name" &&
|
||||
value.numOptions < categorySelectionLimit;
|
||||
|
||||
if (isSelectableCategory) {
|
||||
const categoryOptions = truncateCategories(value.options);
|
||||
return (
|
||||
<Category
|
||||
key={key}
|
||||
metadataField={key}
|
||||
values={categoryOptions}
|
||||
isTruncated={categoryOptions !== value.options}
|
||||
/>
|
||||
);
|
||||
}
|
||||
return undefined;
|
||||
})}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
@@ -2,15 +2,29 @@ import React from "react";
|
||||
import _ from "lodash";
|
||||
import { connect } from "react-redux";
|
||||
import { FaChevronRight, FaChevronDown } from "react-icons/fa";
|
||||
import { Button, Tooltip } from "@blueprintjs/core";
|
||||
import memoize from "memoize-one";
|
||||
import { Button, Tooltip, Position } from "@blueprintjs/core";
|
||||
|
||||
import * as globals from "../../globals";
|
||||
import Value from "./value";
|
||||
import sortedCategoryValues from "./util";
|
||||
import alphabeticallySortedValues from "./util";
|
||||
|
||||
const countCategories = (values, optsAsBools) =>
|
||||
_.reduce(
|
||||
values,
|
||||
(r, v, k) => {
|
||||
r.total += 1;
|
||||
if (optsAsBools[k]) {
|
||||
r.on += 1;
|
||||
}
|
||||
return r;
|
||||
},
|
||||
{ total: 0, on: 0 }
|
||||
);
|
||||
|
||||
@connect(state => ({
|
||||
colorAccessor: state.controls.colorAccessor,
|
||||
categoricalSelectionState: state.controls.categoricalSelectionState
|
||||
categoricalAsBooleansMap: state.controls.categoricalAsBooleansMap
|
||||
}))
|
||||
class Category extends React.Component {
|
||||
constructor(props) {
|
||||
@@ -19,28 +33,24 @@ class Category extends React.Component {
|
||||
isChecked: true,
|
||||
isExpanded: false
|
||||
};
|
||||
this.countCategories = memoize((values, optsAsBools) =>
|
||||
countCategories(values, optsAsBools)
|
||||
);
|
||||
}
|
||||
|
||||
componentDidUpdate() {
|
||||
const { categoricalSelectionState, metadataField } = this.props;
|
||||
const cat = categoricalSelectionState[metadataField];
|
||||
const categoryCount = {
|
||||
// total number of categories in this dimension
|
||||
totalCatCount: cat.numCategories,
|
||||
// number of selected options in this category
|
||||
selectedCatCount: _.reduce(
|
||||
cat.categorySelected,
|
||||
(res, cond) => (cond ? res + 1 : res),
|
||||
0
|
||||
)
|
||||
};
|
||||
if (categoryCount.selectedCatCount === categoryCount.totalCatCount) {
|
||||
const { categoricalAsBooleansMap, metadataField, values } = this.props;
|
||||
const categoryCount = this.countCategories(
|
||||
values,
|
||||
categoricalAsBooleansMap[metadataField]
|
||||
);
|
||||
if (categoryCount.on === categoryCount.total) {
|
||||
/* everything is on, so not indeterminate */
|
||||
this.checkbox.indeterminate = false;
|
||||
} else if (categoryCount.selectedCatCount === 0) {
|
||||
} else if (categoryCount.on === 0) {
|
||||
/* nothing is on, so no */
|
||||
this.checkbox.indeterminate = false;
|
||||
} else if (categoryCount.selectedCatCount < categoryCount.totalCatCount) {
|
||||
} else if (categoryCount.on < categoryCount.total) {
|
||||
/* to be explicit... */
|
||||
this.checkbox.indeterminate = true;
|
||||
}
|
||||
@@ -64,10 +74,11 @@ class Category extends React.Component {
|
||||
}
|
||||
|
||||
toggleNone() {
|
||||
const { dispatch, metadataField } = this.props;
|
||||
const { dispatch, metadataField, value } = this.props;
|
||||
dispatch({
|
||||
type: "categorical metadata filter none of these",
|
||||
metadataField
|
||||
metadataField,
|
||||
value
|
||||
});
|
||||
this.setState({ isChecked: false });
|
||||
}
|
||||
@@ -83,16 +94,13 @@ class Category extends React.Component {
|
||||
}
|
||||
|
||||
renderCategoryItems() {
|
||||
const { categoricalSelectionState, metadataField } = this.props;
|
||||
|
||||
const cat = categoricalSelectionState[metadataField];
|
||||
const optTuples = sortedCategoryValues([...cat.categoryIndices]);
|
||||
return _.map(optTuples, (tuple, i) => (
|
||||
const { values, metadataField } = this.props;
|
||||
return _.map(alphabeticallySortedValues(values), (v, i) => (
|
||||
<Value
|
||||
optTuples={optTuples}
|
||||
key={tuple[1]}
|
||||
key={v}
|
||||
metadataField={metadataField}
|
||||
categoryIndex={tuple[1]}
|
||||
count={values[v]}
|
||||
value={v}
|
||||
i={i}
|
||||
/>
|
||||
));
|
||||
@@ -100,15 +108,12 @@ class Category extends React.Component {
|
||||
|
||||
render() {
|
||||
const { isExpanded, isChecked } = this.state;
|
||||
const {
|
||||
metadataField,
|
||||
colorAccessor,
|
||||
categoricalSelectionState
|
||||
} = this.props;
|
||||
const { isTruncated } = categoricalSelectionState[metadataField];
|
||||
const { metadataField, colorAccessor, isTruncated } = this.props;
|
||||
return (
|
||||
<div
|
||||
style={{
|
||||
// display: "flex",
|
||||
// alignItems: "baseline",
|
||||
maxWidth: globals.maxControlsWidth
|
||||
}}
|
||||
>
|
||||
|
||||
@@ -1,73 +0,0 @@
|
||||
// jshint esversion: 6
|
||||
import React from "react";
|
||||
import _ from "lodash";
|
||||
import { connect } from "react-redux";
|
||||
import * as d3 from "d3";
|
||||
|
||||
@connect()
|
||||
class Occupancy extends React.Component {
|
||||
render() {
|
||||
const {
|
||||
occupancy,
|
||||
colorScale,
|
||||
categoricalSelectionState,
|
||||
colorAccessor,
|
||||
schema
|
||||
} = this.props;
|
||||
const width = 100;
|
||||
const height = 11;
|
||||
|
||||
const categories = _.filter(schema.annotations.obs, {
|
||||
name: colorAccessor
|
||||
})[0].categories;
|
||||
|
||||
const x = d3
|
||||
.scaleLinear()
|
||||
/* get all the keys d[1] as an array, then find the sum */
|
||||
.domain([0, d3.sum(Array.from(occupancy, d => d[1]))])
|
||||
.range([0, width]);
|
||||
|
||||
let currentOffset = 0;
|
||||
|
||||
const stacks = categoricalSelectionState[colorAccessor].categoryValues.map(
|
||||
d => {
|
||||
const o = occupancy.get(d);
|
||||
|
||||
const scaledValue = x(o);
|
||||
|
||||
const stackItem = {
|
||||
key: d,
|
||||
value: o || 0,
|
||||
rectWidth: o ? scaledValue : 0,
|
||||
offset: currentOffset,
|
||||
fill: o ? colorScale(categories.indexOf(d)) : "rgb(255,255,255)"
|
||||
};
|
||||
currentOffset += o ? scaledValue : 0;
|
||||
return stackItem;
|
||||
}
|
||||
);
|
||||
|
||||
return (
|
||||
<svg
|
||||
style={{
|
||||
marginRight: 5,
|
||||
width,
|
||||
height
|
||||
}}
|
||||
>
|
||||
{stacks.map(d => (
|
||||
<rect
|
||||
key={d.key}
|
||||
width={d.rectWidth}
|
||||
height={height}
|
||||
x={d.offset}
|
||||
title={d.metadataField}
|
||||
fill={d.fill}
|
||||
/>
|
||||
))}
|
||||
</svg>
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
export default Occupancy;
|
||||
@@ -1,35 +1,7 @@
|
||||
// jshint esversion: 6
|
||||
|
||||
// values is [ [optVal, optIdx], ...]
|
||||
// index is range array
|
||||
// return sorted index
|
||||
|
||||
import isNumber from "is-number";
|
||||
import _ from "lodash";
|
||||
|
||||
const sortedCategoryValues = values => {
|
||||
/* this sort could be memoized for perf */
|
||||
|
||||
const strings = [];
|
||||
const ints = [];
|
||||
|
||||
_.forEach(values, v => {
|
||||
if (isNumber(v[0])) {
|
||||
ints.push(v);
|
||||
} else {
|
||||
strings.push(v);
|
||||
}
|
||||
});
|
||||
|
||||
strings.sort((a, b) => {
|
||||
const textA = String(a[0]).toUpperCase();
|
||||
const textB = String(b[0]).toUpperCase();
|
||||
export default values =>
|
||||
Object.keys(values).sort((a, b) => {
|
||||
const textA = a.toUpperCase();
|
||||
const textB = b.toUpperCase();
|
||||
return textA < textB ? -1 : textA > textB ? 1 : 0;
|
||||
});
|
||||
|
||||
ints.sort((a, b) => +a[0] - +b[0]);
|
||||
|
||||
return ints.concat(strings);
|
||||
};
|
||||
|
||||
export default sortedCategoryValues;
|
||||
|
||||
@@ -1,77 +1,47 @@
|
||||
// jshint esversion: 6
|
||||
import { connect } from "react-redux";
|
||||
import React from "react";
|
||||
import _ from "lodash";
|
||||
import Occupancy from "./occupancy";
|
||||
import { countCategoryValues2D } from "../../util/stateManager/worldUtil";
|
||||
import * as globals from "../../globals";
|
||||
|
||||
@connect(state => ({
|
||||
categoricalSelectionState: state.controls.categoricalSelectionState,
|
||||
categoricalAsBooleansMap: state.controls.categoricalAsBooleansMap,
|
||||
colorScale: state.controls.colorScale,
|
||||
colorAccessor: state.controls.colorAccessor,
|
||||
schema: _.get(state.controls.world, "schema", null),
|
||||
world: state.controls.world
|
||||
colorAccessor: state.controls.colorAccessor
|
||||
}))
|
||||
class CategoryValue extends React.Component {
|
||||
toggleOff() {
|
||||
const { dispatch, metadataField, categoryIndex } = this.props;
|
||||
const { dispatch, metadataField, value } = this.props;
|
||||
dispatch({
|
||||
type: "categorical metadata filter deselect",
|
||||
metadataField,
|
||||
categoryIndex
|
||||
value
|
||||
});
|
||||
}
|
||||
|
||||
toggleOn() {
|
||||
const { dispatch, metadataField, categoryIndex } = this.props;
|
||||
const { dispatch, metadataField, value } = this.props;
|
||||
dispatch({
|
||||
type: "categorical metadata filter select",
|
||||
metadataField,
|
||||
categoryIndex
|
||||
value
|
||||
});
|
||||
}
|
||||
|
||||
render() {
|
||||
const {
|
||||
categoricalSelectionState,
|
||||
categoricalAsBooleansMap,
|
||||
metadataField,
|
||||
categoryIndex,
|
||||
count,
|
||||
value,
|
||||
colorAccessor,
|
||||
colorScale,
|
||||
i,
|
||||
schema,
|
||||
world
|
||||
i
|
||||
} = this.props;
|
||||
|
||||
if (!categoricalSelectionState) return null;
|
||||
|
||||
const category = categoricalSelectionState[metadataField];
|
||||
const selected = category.categorySelected[categoryIndex];
|
||||
const count = category.categoryCounts[categoryIndex];
|
||||
const value = category.categoryValues[categoryIndex];
|
||||
const displayString = String(
|
||||
category.categoryValues[categoryIndex]
|
||||
).valueOf();
|
||||
if (!categoricalAsBooleansMap) return null;
|
||||
|
||||
const selected = categoricalAsBooleansMap[metadataField][value];
|
||||
/* this is the color scale, so add swatches below */
|
||||
const isColorBy = metadataField === colorAccessor;
|
||||
let categories = null;
|
||||
let occupancy = null;
|
||||
|
||||
if (isColorBy && schema) {
|
||||
categories = _.filter(schema.annotations.obs, {
|
||||
name: colorAccessor
|
||||
})[0].categories;
|
||||
}
|
||||
|
||||
if (colorAccessor && !isColorBy) {
|
||||
occupancy = countCategoryValues2D(
|
||||
metadataField,
|
||||
colorAccessor,
|
||||
world.obsAnnotations
|
||||
);
|
||||
}
|
||||
const c = metadataField === colorAccessor;
|
||||
|
||||
return (
|
||||
<div
|
||||
@@ -86,10 +56,7 @@ class CategoryValue extends React.Component {
|
||||
style={{
|
||||
margin: 0,
|
||||
padding: 0,
|
||||
userSelect: "none",
|
||||
width: globals.leftSidebarWidth - 130,
|
||||
display: "flex",
|
||||
justifyContent: "space-between"
|
||||
userSelect: "none"
|
||||
}}
|
||||
>
|
||||
<label className="bp3-control bp3-checkbox">
|
||||
@@ -101,20 +68,8 @@ class CategoryValue extends React.Component {
|
||||
type="checkbox"
|
||||
/>
|
||||
<span className="bp3-control-indicator" />
|
||||
{displayString}
|
||||
{value}
|
||||
</label>
|
||||
<span style={{ flexShrink: 0 }}>
|
||||
{colorAccessor &&
|
||||
!isColorBy &&
|
||||
categoricalSelectionState[colorAccessor] ? (
|
||||
<Occupancy
|
||||
occupancy={occupancy.get(
|
||||
category.categoryValues[categoryIndex]
|
||||
)}
|
||||
{...this.props}
|
||||
/>
|
||||
) : null}
|
||||
</span>
|
||||
</div>
|
||||
<span>
|
||||
<span>{count}</span>
|
||||
@@ -123,10 +78,7 @@ class CategoryValue extends React.Component {
|
||||
marginLeft: 5,
|
||||
width: 11,
|
||||
height: 11,
|
||||
backgroundColor:
|
||||
isColorBy && categories
|
||||
? colorScale(categories.indexOf(value))
|
||||
: "inherit"
|
||||
backgroundColor: c ? colorScale(value) : "inherit"
|
||||
}}
|
||||
/>
|
||||
</span>
|
||||
|
||||
@@ -2,7 +2,7 @@
|
||||
import React from "react";
|
||||
import { connect } from "react-redux";
|
||||
import * as d3 from "d3";
|
||||
import { interpolateViridis, interpolateCool } from "d3-scale-chromatic";
|
||||
import { interpolateViridis } from "d3-scale-chromatic";
|
||||
|
||||
// create continuous color legend
|
||||
// http://bl.ocks.org/syntagmatic/e8ccca52559796be775553b467593a9f
|
||||
@@ -121,12 +121,12 @@ class ContinuousLegend extends React.Component {
|
||||
.remove();
|
||||
}
|
||||
|
||||
if (colorAccessor && colorScale && colorScale.range) {
|
||||
if (colorAccessor && colorScale) {
|
||||
/* fragile! continuous range is 0 to 1, not [#fa4b2c, ...], make this a flag? */
|
||||
if (colorScale.range()[0][0] !== "#") {
|
||||
continuous(
|
||||
"#continuous_legend",
|
||||
d3.scaleSequential(interpolateCool).domain(colorScale.domain()),
|
||||
d3.scaleSequential(interpolateViridis).domain(colorScale.domain()),
|
||||
colorAccessor
|
||||
);
|
||||
}
|
||||
|
||||
@@ -4,51 +4,14 @@
|
||||
import React from "react";
|
||||
import _ from "lodash";
|
||||
import * as d3 from "d3";
|
||||
import fuzzysort from "fuzzysort";
|
||||
|
||||
import { connect } from "react-redux";
|
||||
import { MenuItem } from "@blueprintjs/core";
|
||||
import { Suggest } from "@blueprintjs/select";
|
||||
import { Button, Tooltip } from "@blueprintjs/core";
|
||||
import HistogramBrush from "../brushableHistogram";
|
||||
import * as globals from "../../globals";
|
||||
import actions from "../../actions";
|
||||
import { postUserErrorToast } from "../framework/toasters";
|
||||
import ExpressionButtons from "./expressionButtons";
|
||||
|
||||
const renderGene = (fuzzySortResult, { handleClick, modifiers, query }) => {
|
||||
if (!modifiers.matchesPredicate) {
|
||||
return null;
|
||||
}
|
||||
/* the fuzzysort wraps the object with other properties, like a score */
|
||||
const gene = fuzzySortResult.obj;
|
||||
const text = gene.name;
|
||||
|
||||
return (
|
||||
<MenuItem
|
||||
active={modifiers.active}
|
||||
disabled={modifiers.disabled}
|
||||
// Use of annotations in this way is incorrect and dataset specific.
|
||||
// See https://github.com/chanzuckerberg/cellxgene/issues/483
|
||||
// label={gene.n_counts}
|
||||
key={gene.name}
|
||||
onClick={g => {
|
||||
/* this fires when user clicks a menu item */
|
||||
handleClick(g);
|
||||
}}
|
||||
text={text}
|
||||
/>
|
||||
);
|
||||
};
|
||||
|
||||
const filterGenes = (query, genes) => {
|
||||
/* fires on load, once, and then for each character typed into the input */
|
||||
return fuzzysort.go(query, genes, {
|
||||
key: "name",
|
||||
limit: 5,
|
||||
threshold: -10000 // don't return bad results
|
||||
});
|
||||
};
|
||||
|
||||
@connect(state => {
|
||||
const metadata = _.get(state.controls.world, "obsAnnotations", null);
|
||||
const ranges = _.get(state.controls.world, "summary.obs", null);
|
||||
@@ -66,9 +29,23 @@ const filterGenes = (query, genes) => {
|
||||
};
|
||||
})
|
||||
class GeneExpression extends React.Component {
|
||||
handleClick(g) {
|
||||
constructor(props) {
|
||||
super(props);
|
||||
this.state = {
|
||||
gene: ""
|
||||
};
|
||||
}
|
||||
|
||||
keyPress(e) {
|
||||
if (e.keyCode === 13) {
|
||||
this.handleClick();
|
||||
}
|
||||
}
|
||||
|
||||
handleClick() {
|
||||
const { world, dispatch, userDefinedGenes } = this.props;
|
||||
const gene = g.target;
|
||||
const { gene } = this.state;
|
||||
|
||||
if (userDefinedGenes.indexOf(gene) !== -1) {
|
||||
postUserErrorToast("That gene already exists");
|
||||
} else if (userDefinedGenes.length > 15) {
|
||||
@@ -83,11 +60,13 @@ class GeneExpression extends React.Component {
|
||||
type: "user defined gene",
|
||||
data: gene
|
||||
});
|
||||
this.setState({ gene: "" });
|
||||
}
|
||||
}
|
||||
|
||||
render() {
|
||||
const { world, userDefinedGenes, differential } = this.props;
|
||||
const { gene } = this.state;
|
||||
|
||||
return (
|
||||
<div>
|
||||
@@ -108,27 +87,27 @@ class GeneExpression extends React.Component {
|
||||
style={{ padding: globals.leftSidebarSectionPadding }}
|
||||
className="bp3-control-group"
|
||||
>
|
||||
<Suggest
|
||||
closeOnSelect
|
||||
openOnKeyDown
|
||||
resetOnSelect
|
||||
noResults={<MenuItem disabled text="No matching genes." />}
|
||||
onItemSelect={g => {
|
||||
/* this happens on 'enter' */
|
||||
this.handleClick(g);
|
||||
}}
|
||||
inputValueRenderer={g => {
|
||||
return "";
|
||||
}}
|
||||
itemListPredicate={filterGenes}
|
||||
itemRenderer={renderGene.bind(this)}
|
||||
items={
|
||||
world && world.varAnnotations
|
||||
? world.varAnnotations
|
||||
: [{ name: "No genes", n_counts: "" }]
|
||||
}
|
||||
popoverProps={{ minimal: true }}
|
||||
/>
|
||||
<div className="bp3-input-group bp3-fill">
|
||||
<input
|
||||
onKeyDown={this.keyPress.bind(this)}
|
||||
onChange={e => {
|
||||
this.setState({ gene: e.target.value });
|
||||
}}
|
||||
value={gene}
|
||||
type="text"
|
||||
className="bp3-input"
|
||||
placeholder="Enter a gene name"
|
||||
style={{ paddingRight: 94 }}
|
||||
/>
|
||||
</div>
|
||||
<Tooltip
|
||||
content="Add a gene to see its expression levels"
|
||||
position="bottom"
|
||||
>
|
||||
<Button intent="primary" onClick={this.handleClick.bind(this)}>
|
||||
Add
|
||||
</Button>
|
||||
</Tooltip>
|
||||
</div>
|
||||
{world && userDefinedGenes.length > 0
|
||||
? _.map(userDefinedGenes, (geneName, index) => {
|
||||
@@ -173,9 +152,9 @@ class GeneExpression extends React.Component {
|
||||
zebra={index % 2 === 0}
|
||||
ranges={d3.extent(values)}
|
||||
isDiffExp
|
||||
logFoldChange={value[1]}
|
||||
pval={value[2]}
|
||||
pvalAdj={value[3]}
|
||||
avgDiff={value[1]}
|
||||
set1AvgExp={value[4]}
|
||||
set2AvgExp={value[5]}
|
||||
/>
|
||||
);
|
||||
})
|
||||
|
||||
@@ -38,7 +38,7 @@ export default function(regl) {
|
||||
uniforms: {
|
||||
distance: regl.prop("distance"),
|
||||
view: regl.prop("view"),
|
||||
projection: ({viewportWidth, viewportHeight}) => mat4.perspective([], Math.PI / 2, viewportWidth / viewportHeight, 0.01, 1000)
|
||||
projection: () => mat4.perspective([], Math.PI / 2, 1, 0.01, 1000)
|
||||
},
|
||||
|
||||
count: regl.prop("count"),
|
||||
|
||||
@@ -6,7 +6,8 @@ import { connect } from "react-redux";
|
||||
import mat4 from "gl-mat4";
|
||||
import _regl from "regl";
|
||||
import { Button, AnchorButton, Tooltip } from "@blueprintjs/core";
|
||||
import * as globals from "../../globals";
|
||||
import { worldEqUniverse } from "../../util/stateManager/world";
|
||||
|
||||
import setupSVGandBrushElements from "./setupSVGandBrush";
|
||||
import actions from "../../actions";
|
||||
import _camera from "../../util/camera";
|
||||
@@ -29,9 +30,9 @@ class Graph extends React.Component {
|
||||
super(props);
|
||||
this.count = 0;
|
||||
this.inverse = mat4.identity([]);
|
||||
this.graphPaddingTop = 0;
|
||||
this.graphPaddingTop = 100;
|
||||
this.graphPaddingBottom = 45;
|
||||
this.graphPaddingRight = globals.leftSidebarWidth;
|
||||
this.graphPaddingRight = 10;
|
||||
this.renderCache = {
|
||||
positions: null,
|
||||
colors: null
|
||||
@@ -125,24 +126,18 @@ class Graph extends React.Component {
|
||||
const glScaleX = scaleLinear([0, 1], [-1, 1]);
|
||||
const glScaleY = scaleLinear([0, 1], [1, -1]);
|
||||
|
||||
const offset = [d3.mean(obsLayout.X) - 0.5, d3.mean(obsLayout.Y) - 0.5];
|
||||
|
||||
for (
|
||||
let i = 0, { positions } = this.renderCache;
|
||||
i < cellCount;
|
||||
i += 1
|
||||
) {
|
||||
positions[2 * i] = glScaleX(obsLayout.X[i] - offset[0]);
|
||||
positions[2 * i + 1] = glScaleY(obsLayout.Y[i] - offset[1]);
|
||||
positions[2 * i] = glScaleX(obsLayout.X[i]);
|
||||
positions[2 * i + 1] = glScaleY(obsLayout.Y[i]);
|
||||
}
|
||||
pointBuffer({
|
||||
data: this.renderCache.positions,
|
||||
dimension: 2
|
||||
});
|
||||
|
||||
this.setState({
|
||||
offset
|
||||
});
|
||||
}
|
||||
|
||||
// Colors for each point - a cached value that only changes when
|
||||
@@ -201,7 +196,7 @@ class Graph extends React.Component {
|
||||
this.handleBrushSelectAction.bind(this),
|
||||
this.handleBrushDeselectAction.bind(this),
|
||||
responsive,
|
||||
this.graphPaddingRight
|
||||
this.graphPaddingTop
|
||||
);
|
||||
this.setState({ svg: newSvg, brush });
|
||||
}
|
||||
@@ -256,7 +251,7 @@ class Graph extends React.Component {
|
||||
an event on procedural deselect because it is move: null
|
||||
*/
|
||||
|
||||
const { camera, offset } = this.state;
|
||||
const { camera } = this.state;
|
||||
const { dispatch, responsive } = this.props;
|
||||
|
||||
if (d3.event.sourceEvent !== null) {
|
||||
@@ -267,7 +262,6 @@ class Graph extends React.Component {
|
||||
https://bl.ocks.org/EfratVil/0e542f5fc426065dd1d4b6daaa345a9f
|
||||
*/
|
||||
const s = d3.event.selection;
|
||||
const gl = this.state.regl._gl;
|
||||
/*
|
||||
event describing brush position:
|
||||
@-------|
|
||||
@@ -276,23 +270,19 @@ class Graph extends React.Component {
|
||||
|-------@
|
||||
*/
|
||||
|
||||
// get aspect ratio
|
||||
const aspect = gl.drawingBufferWidth / gl.drawingBufferHeight;
|
||||
|
||||
// compute inverse view matrix
|
||||
const inverse = mat4.invert([], camera.view());
|
||||
|
||||
// transform screen coordinates -> cell coordinates
|
||||
const invert = pin => {
|
||||
const x =
|
||||
(2 * pin[0]) / (responsive.width - this.graphPaddingRight) - 1;
|
||||
const x = (2 * pin[0]) / (responsive.height - this.graphPaddingTop) - 1;
|
||||
const y =
|
||||
2 * (1 - pin[1] / (responsive.height - this.graphPaddingTop)) - 1;
|
||||
const pout = [
|
||||
x * inverse[14] * aspect + inverse[12],
|
||||
x * inverse[14] + inverse[12],
|
||||
y * inverse[14] + inverse[13]
|
||||
];
|
||||
return [(pout[0] + 1) / 2 + offset[0], (pout[1] + 1) / 2 + offset[1]];
|
||||
return [(pout[0] + 1) / 2, (pout[1] + 1) / 2];
|
||||
};
|
||||
|
||||
const brushCoords = {
|
||||
@@ -376,7 +366,6 @@ class Graph extends React.Component {
|
||||
style={{ marginRight: 10 }}
|
||||
onClick={() => {
|
||||
dispatch(actions.regraph());
|
||||
dispatch({ type: "increment graph render counter" });
|
||||
}}
|
||||
>
|
||||
subset to current selection
|
||||
@@ -432,10 +421,12 @@ class Graph extends React.Component {
|
||||
</div>
|
||||
<div
|
||||
style={{
|
||||
marginRight: 50,
|
||||
marginTop: 50,
|
||||
zIndex: -9999,
|
||||
position: "fixed",
|
||||
top: 0,
|
||||
right: 0
|
||||
right: this.graphPaddingRight,
|
||||
bottom: this.graphPaddingBottom
|
||||
}}
|
||||
>
|
||||
<div
|
||||
@@ -446,7 +437,7 @@ class Graph extends React.Component {
|
||||
/>
|
||||
<div style={{ padding: 0, margin: 0 }}>
|
||||
<canvas
|
||||
width={responsive.width - this.graphPaddingRight}
|
||||
width={responsive.height - this.graphPaddingTop}
|
||||
height={responsive.height - this.graphPaddingTop}
|
||||
ref={canvas => {
|
||||
this.reglCanvas = canvas;
|
||||
|
||||
@@ -12,18 +12,22 @@ export default (
|
||||
handleBrushSelectAction,
|
||||
handleBrushDeselectAction,
|
||||
responsive,
|
||||
graphPaddingRight
|
||||
graphPaddingTop
|
||||
) => {
|
||||
const side = responsive.height - graphPaddingTop;
|
||||
const svg = d3
|
||||
.select("#graphAttachPoint")
|
||||
.append("svg")
|
||||
.attr("width", responsive.width - graphPaddingRight)
|
||||
.attr("height", responsive.height)
|
||||
.attr("width", side)
|
||||
.attr("height", side)
|
||||
.attr("class", `${styles.graphSVG}`);
|
||||
|
||||
const brush = d3
|
||||
.brush()
|
||||
.extent([[0, 0], [responsive.width - graphPaddingRight, responsive.height]])
|
||||
.extent([
|
||||
[0, 0],
|
||||
[responsive.height - graphPaddingTop, responsive.height - graphPaddingTop]
|
||||
])
|
||||
.on("brush", handleBrushSelectAction)
|
||||
.on("end", handleBrushDeselectAction);
|
||||
|
||||
|
||||
@@ -38,7 +38,14 @@ export default function(regl) {
|
||||
uniforms: {
|
||||
distance: regl.prop("distance"),
|
||||
view: regl.prop("view"),
|
||||
projection: () => mat4.perspective([], Math.PI / 2, 1, 0.01, 1000)
|
||||
projection: (context, props) =>
|
||||
mat4.perspective(
|
||||
[],
|
||||
Math.PI / 2,
|
||||
(context.viewportWidth * props.scale) / context.viewportHeight,
|
||||
0.01,
|
||||
1000
|
||||
)
|
||||
},
|
||||
|
||||
count: regl.prop("count"),
|
||||
|
||||
@@ -82,6 +82,12 @@ class Scatterplot extends React.Component {
|
||||
this.drawAxesSVG(scales.xScale, scales.yScale, svg);
|
||||
}
|
||||
|
||||
this.setState({
|
||||
svg,
|
||||
xScale: scales ? scales.xScale : null,
|
||||
yScale: scales ? scales.yScale : null
|
||||
});
|
||||
|
||||
const camera = _camera(this.reglCanvas, { scale: true, rotate: false });
|
||||
const regl = _regl(this.reglCanvas);
|
||||
|
||||
@@ -92,35 +98,43 @@ class Scatterplot extends React.Component {
|
||||
const colorBuffer = regl.buffer();
|
||||
const sizeBuffer = regl.buffer();
|
||||
|
||||
const reglRender = regl.frame(() => {
|
||||
this.reglDraw(
|
||||
regl,
|
||||
drawPoints,
|
||||
sizeBuffer,
|
||||
colorBuffer,
|
||||
pointBuffer,
|
||||
camera
|
||||
);
|
||||
regl.frame(({ viewportWidth, viewportHeight }) => {
|
||||
regl.clear({
|
||||
depth: 1,
|
||||
color: [1, 1, 1, 1]
|
||||
});
|
||||
|
||||
drawPoints({
|
||||
distance: camera.distance,
|
||||
color: colorBuffer,
|
||||
position: pointBuffer,
|
||||
size: sizeBuffer,
|
||||
count: this.count,
|
||||
view: camera.view(),
|
||||
scale: viewportHeight / viewportWidth
|
||||
});
|
||||
|
||||
camera.tick();
|
||||
});
|
||||
|
||||
this.reglRenderState = "rendering";
|
||||
|
||||
this.setState({
|
||||
regl,
|
||||
sizeBuffer,
|
||||
pointBuffer,
|
||||
colorBuffer,
|
||||
svg,
|
||||
xScale: scales ? scales.xScale : null,
|
||||
yScale: scales ? scales.yScale : null,
|
||||
reglRender,
|
||||
camera,
|
||||
drawPoints
|
||||
colorBuffer
|
||||
});
|
||||
}
|
||||
|
||||
componentDidUpdate(prevProps) {
|
||||
const {
|
||||
svg,
|
||||
xScale,
|
||||
yScale,
|
||||
regl,
|
||||
pointBuffer,
|
||||
colorBuffer,
|
||||
sizeBuffer
|
||||
} = this.state;
|
||||
const {
|
||||
world,
|
||||
crossfilter,
|
||||
@@ -130,18 +144,6 @@ class Scatterplot extends React.Component {
|
||||
expressionY,
|
||||
colorRGB
|
||||
} = this.props;
|
||||
const {
|
||||
reglRender,
|
||||
xScale,
|
||||
yScale,
|
||||
regl,
|
||||
pointBuffer,
|
||||
colorBuffer,
|
||||
sizeBuffer,
|
||||
svg,
|
||||
drawPoints,
|
||||
camera
|
||||
} = this.state;
|
||||
|
||||
if (
|
||||
world &&
|
||||
@@ -157,11 +159,6 @@ class Scatterplot extends React.Component {
|
||||
this.drawAxesSVG(xScale, yScale, svg);
|
||||
}
|
||||
|
||||
if (reglRender && this.reglRenderState === "rendering") {
|
||||
reglRender.cancel();
|
||||
this.reglRenderState = "paused";
|
||||
}
|
||||
|
||||
if (
|
||||
world &&
|
||||
regl &&
|
||||
@@ -201,16 +198,6 @@ class Scatterplot extends React.Component {
|
||||
colorBuffer({ data: colorsBuf, dimension: 3 });
|
||||
sizeBuffer({ data: sizesBuf, dimension: 1 });
|
||||
this.count = cellCount;
|
||||
|
||||
regl._refresh();
|
||||
this.reglDraw(
|
||||
regl,
|
||||
drawPoints,
|
||||
sizeBuffer,
|
||||
colorBuffer,
|
||||
pointBuffer,
|
||||
camera
|
||||
);
|
||||
}
|
||||
|
||||
if (
|
||||
@@ -240,22 +227,6 @@ class Scatterplot extends React.Component {
|
||||
};
|
||||
}
|
||||
|
||||
reglDraw(regl, drawPoints, sizeBuffer, colorBuffer, pointBuffer, camera) {
|
||||
regl.clear({
|
||||
depth: 1,
|
||||
color: [1, 1, 1, 1]
|
||||
});
|
||||
|
||||
drawPoints({
|
||||
size: sizeBuffer,
|
||||
distance: camera.distance,
|
||||
color: colorBuffer,
|
||||
position: pointBuffer,
|
||||
count: this.count,
|
||||
view: camera.view()
|
||||
});
|
||||
}
|
||||
|
||||
drawAxesSVG(xScale, yScale, svg) {
|
||||
const { scatterplotYYaccessor, scatterplotXXaccessor } = this.props;
|
||||
svg.selectAll("*").remove();
|
||||
|
||||
@@ -1,13 +1,7 @@
|
||||
// jshint esversion: 6
|
||||
import _ from "lodash";
|
||||
import * as d3 from "d3";
|
||||
import {
|
||||
interpolateViridis,
|
||||
interpolateSpectral,
|
||||
interpolateRainbow,
|
||||
interpolateBlues,
|
||||
interpolateCool
|
||||
} from "d3-scale-chromatic";
|
||||
import { interpolateViridis } from "d3-scale-chromatic";
|
||||
import * as globals from "../globals";
|
||||
import parseRGB from "../util/parseRGB";
|
||||
|
||||
@@ -65,17 +59,11 @@ const updateCellColorsMiddleware = store => next => action => {
|
||||
*/
|
||||
|
||||
if (action.type === "color by categorical metadata") {
|
||||
const categories = _.filter(s.controls.world.schema.annotations.obs, {
|
||||
name: action.colorAccessor
|
||||
})[0].categories;
|
||||
|
||||
colorScale = d3
|
||||
.scaleSequential(interpolateRainbow)
|
||||
.domain([0, categories.length]);
|
||||
colorScale = d3.scaleOrdinal().range(globals.ordinalColors);
|
||||
|
||||
for (let i = 0; i < obsAnnotations.length; i += 1) {
|
||||
const obs = obsAnnotations[i];
|
||||
const c = colorScale(categories.indexOf(obs[action.colorAccessor]));
|
||||
const c = colorScale(obs[action.colorAccessor]);
|
||||
colorsByName[i] = c;
|
||||
colorsByRGB[i] = parseRGB(c);
|
||||
}
|
||||
@@ -89,7 +77,7 @@ const updateCellColorsMiddleware = store => next => action => {
|
||||
|
||||
for (let i = 0; i < obsAnnotations.length; i += 1) {
|
||||
const obs = obsAnnotations[i];
|
||||
const c = interpolateCool(colorScale(obs[action.colorAccessor]));
|
||||
const c = interpolateViridis(colorScale(obs[action.colorAccessor]));
|
||||
colorsByName[i] = c;
|
||||
colorsByRGB[i] = parseRGB(c);
|
||||
}
|
||||
@@ -107,7 +95,7 @@ const updateCellColorsMiddleware = store => next => action => {
|
||||
]); /* invert viridis... probably pass this scale through to others */
|
||||
|
||||
for (let i = 0, len = expression.length; i < len; i += 1) {
|
||||
const c = interpolateCool(colorScale(expression[i]));
|
||||
const c = interpolateViridis(colorScale(expression[i]));
|
||||
colorsByName[i] = c;
|
||||
colorsByRGB[i] = parseRGB(c);
|
||||
}
|
||||
|
||||
Vendored
+57
-164
@@ -1,7 +1,7 @@
|
||||
// jshint esversion: 6
|
||||
|
||||
import _ from "lodash";
|
||||
import { World, kvCache, WorldUtil } from "../util/stateManager";
|
||||
import { World, kvCache } from "../util/stateManager";
|
||||
import parseRGB from "../util/parseRGB";
|
||||
import Crossfilter from "../util/typedCrossfilter";
|
||||
import * as globals from "../globals";
|
||||
@@ -12,109 +12,34 @@ import {
|
||||
diffexpDimensionName,
|
||||
makeContinuousDimensionName
|
||||
} from "../util/nameCreators";
|
||||
import { fillRange } from "../util/typedCrossfilter/util";
|
||||
|
||||
/*
|
||||
Selection state for categoricals are tracked in an Object that
|
||||
has two main components for each category:
|
||||
1. mapping of option value to an index
|
||||
2. array of bool selection state by index
|
||||
Remember that option values can be ANY js type, except undefined/null.
|
||||
|
||||
{
|
||||
_category_name_1: {
|
||||
// map of option value to index
|
||||
categoryIndices: Map([
|
||||
catval1: index,
|
||||
...
|
||||
])
|
||||
|
||||
// index->selection true/false state
|
||||
categorySelected: [ true/false, true/false, ... ]
|
||||
|
||||
// number of options
|
||||
numCategories: number,
|
||||
|
||||
// isTruncated - true if the options for selection has
|
||||
// been truncated (ie, was too large to implement)
|
||||
}
|
||||
}
|
||||
*/
|
||||
function topNCategories(summary) {
|
||||
const counts = _.map(summary.categories, cat =>
|
||||
summary.categoryCounts.get(cat)
|
||||
);
|
||||
const sortIndex = fillRange(new Array(summary.numCategories)).sort(
|
||||
(a, b) => counts[b] - counts[a]
|
||||
);
|
||||
const sortedCategories = _.map(sortIndex, i => summary.categories[i]);
|
||||
const sortedCounts = _.map(sortIndex, i => counts[i]);
|
||||
const N = globals.maxCategoricalOptionsToDisplay;
|
||||
|
||||
if (sortedCategories.length < N) {
|
||||
return [sortedCategories, sortedCounts];
|
||||
}
|
||||
return [sortedCategories.slice(0, N), sortedCounts.slice(0, N)];
|
||||
}
|
||||
|
||||
function createCategoricalSelectionState(state, world) {
|
||||
function createCategoricalAsBooleansMap(world) {
|
||||
const res = {};
|
||||
_.forEach(world.summary.obs, (value, key) => {
|
||||
if (value.categories) {
|
||||
const isColorField = key.includes("color") || key.includes("Color");
|
||||
const isSelectableCategory =
|
||||
!isColorField &&
|
||||
key !== "name" &&
|
||||
value.categories.length < state.maxCategoryItems;
|
||||
if (isSelectableCategory) {
|
||||
const [categoryValues, categoryCounts] = topNCategories(value);
|
||||
const categoryIndices = new Map(categoryValues.map((v, i) => [v, i]));
|
||||
const numCategories = categoryIndices.size;
|
||||
const categorySelected = new Array(numCategories).fill(true);
|
||||
const isTruncated = categoryValues.length < value.numCategories;
|
||||
res[key] = {
|
||||
categoryValues, // array: of natively typed category values
|
||||
categoryIndices, // map: category value (native type) -> category index
|
||||
categorySelected, // array: t/f selection state
|
||||
numCategories, // number: of categories
|
||||
isTruncated, // bool: true if list was truncated
|
||||
categoryCounts // array: cardinality of each category
|
||||
};
|
||||
}
|
||||
_.each(world.summary.obs, (value, key) => {
|
||||
if (value.options && key !== "name") {
|
||||
const optionsAsBooleans = {};
|
||||
_.each(value.options, (_value, _key) => {
|
||||
optionsAsBooleans[_key] = true;
|
||||
});
|
||||
res[key] = optionsAsBooleans;
|
||||
}
|
||||
});
|
||||
return res;
|
||||
}
|
||||
|
||||
/*
|
||||
given a categoricalSelectionState, return the list of all category values
|
||||
where selection state is true (ie, they are selected).
|
||||
*/
|
||||
function selectedValuesForCategory(categorySelectionState) {
|
||||
const selectedValues = _([...categorySelectionState.categoryIndices])
|
||||
.filter(tuple => categorySelectionState.categorySelected[tuple[1]])
|
||||
.map(tuple => tuple[0])
|
||||
.value();
|
||||
return selectedValues;
|
||||
}
|
||||
|
||||
const Controls = (
|
||||
state = {
|
||||
// data loading flag
|
||||
loading: false,
|
||||
error: null,
|
||||
|
||||
// configuration
|
||||
maxCategoryItems: globals.configDefaults.parameters["max-category-items"],
|
||||
|
||||
// the whole big bang
|
||||
universe: null,
|
||||
|
||||
// all of the data + selection state
|
||||
world: null,
|
||||
colorName: null,
|
||||
colorRGB: null,
|
||||
categoricalSelectionState: null,
|
||||
categoricalAsBooleansMap: null,
|
||||
crossfilter: null,
|
||||
dimensionMap: null,
|
||||
userDefinedGenes: [],
|
||||
@@ -129,7 +54,6 @@ const Controls = (
|
||||
scatterplotXXaccessor: null, // just easier to read
|
||||
scatterplotYYaccessor: null,
|
||||
axesHaveBeenDrawn: false,
|
||||
graphRenderCounter: 0 /* integer as <Component key={graphRenderCounter} - a change in key forces a remount */,
|
||||
__storedStateForCelllist1__: null /* will need procedural control of brush ie., brush.extent https://bl.ocks.org/micahstubbs/3cda05ca68cba260cb81 */,
|
||||
__storedStateForCelllist2__: null
|
||||
},
|
||||
@@ -147,17 +71,6 @@ const Controls = (
|
||||
Initialization, World/Universe management
|
||||
and data loading.
|
||||
******************************************************/
|
||||
case "configuration load complete": {
|
||||
// there are a couple of configuration items we need to retain
|
||||
return {
|
||||
...state,
|
||||
maxCategoryItems: _.get(
|
||||
state.config,
|
||||
"parameters.max-category-items",
|
||||
globals.configDefaults.parameters["max-category-items"]
|
||||
)
|
||||
};
|
||||
}
|
||||
case "initial data load start": {
|
||||
return { ...state, loading: true };
|
||||
}
|
||||
@@ -169,13 +82,9 @@ const Controls = (
|
||||
const world = World.createWorldFromEntireUniverse(universe);
|
||||
const colorName = new Array(universe.nObs).fill(globals.defaultCellColor);
|
||||
const colorRGB = _.map(colorName, c => parseRGB(c));
|
||||
const categoricalSelectionState = createCategoricalSelectionState(
|
||||
state,
|
||||
world
|
||||
);
|
||||
const categoricalAsBooleansMap = createCategoricalAsBooleansMap(world);
|
||||
const crossfilter = Crossfilter(world.obsAnnotations);
|
||||
const dimensionMap = World.createObsDimensionMap(crossfilter, world);
|
||||
WorldUtil.clearCaches();
|
||||
|
||||
const worldVarDataCache = world.varDataCache;
|
||||
|
||||
@@ -225,7 +134,7 @@ const Controls = (
|
||||
world,
|
||||
colorName,
|
||||
colorRGB,
|
||||
categoricalSelectionState,
|
||||
categoricalAsBooleansMap,
|
||||
crossfilter,
|
||||
dimensionMap,
|
||||
colorAccessor: null
|
||||
@@ -242,13 +151,9 @@ const Controls = (
|
||||
);
|
||||
const colorName = new Array(world.nObs).fill(globals.defaultCellColor);
|
||||
const colorRGB = _.map(colorName, c => parseRGB(c));
|
||||
const categoricalSelectionState = createCategoricalSelectionState(
|
||||
state,
|
||||
world
|
||||
);
|
||||
const categoricalAsBooleansMap = createCategoricalAsBooleansMap(world);
|
||||
const crossfilter = Crossfilter(world.obsAnnotations);
|
||||
const dimensionMap = World.createObsDimensionMap(crossfilter, world);
|
||||
WorldUtil.clearCaches();
|
||||
|
||||
const worldVarDataCache = world.varDataCache;
|
||||
/* var dimensions */
|
||||
@@ -291,7 +196,7 @@ const Controls = (
|
||||
world,
|
||||
colorName,
|
||||
colorRGB,
|
||||
categoricalSelectionState,
|
||||
categoricalAsBooleansMap,
|
||||
crossfilter,
|
||||
dimensionMap,
|
||||
colorAccessor: null
|
||||
@@ -505,100 +410,88 @@ const Controls = (
|
||||
...state,
|
||||
opacityForDeselectedCells: action.data
|
||||
};
|
||||
case "increment graph render counter": {
|
||||
const c = state.graphRenderCounter + 1;
|
||||
return {
|
||||
...state,
|
||||
graphRenderCounter: c
|
||||
};
|
||||
}
|
||||
|
||||
/*******************************
|
||||
Categorical metadata
|
||||
*******************************/
|
||||
case "categorical metadata filter select": {
|
||||
const newCategorySelected = Array.from(
|
||||
state.categoricalSelectionState[action.metadataField].categorySelected
|
||||
);
|
||||
newCategorySelected[action.categoryIndex] = true;
|
||||
const newCategoricalSelectionState = {
|
||||
...state.categoricalSelectionState,
|
||||
const newCategoricalAsBooleansMap = {
|
||||
...state.categoricalAsBooleansMap,
|
||||
[action.metadataField]: {
|
||||
...state.categoricalSelectionState[action.metadataField],
|
||||
categorySelected: newCategorySelected
|
||||
...state.categoricalAsBooleansMap[action.metadataField],
|
||||
[action.value]: true
|
||||
}
|
||||
};
|
||||
|
||||
// update the filter to match all selected options
|
||||
const cat = newCategoricalSelectionState[action.metadataField];
|
||||
// update the filter for the one category that changed state
|
||||
state.dimensionMap[obsAnnoDimensionName(action.metadataField)].filterEnum(
|
||||
selectedValuesForCategory(cat)
|
||||
_.filter(
|
||||
_.map(
|
||||
newCategoricalAsBooleansMap[action.metadataField],
|
||||
(val, key) => (val ? key : false)
|
||||
)
|
||||
)
|
||||
);
|
||||
|
||||
return {
|
||||
...state,
|
||||
categoricalSelectionState: newCategoricalSelectionState
|
||||
categoricalAsBooleansMap: newCategoricalAsBooleansMap
|
||||
};
|
||||
}
|
||||
case "categorical metadata filter deselect": {
|
||||
const newCategorySelected = Array.from(
|
||||
state.categoricalSelectionState[action.metadataField].categorySelected
|
||||
);
|
||||
newCategorySelected[action.categoryIndex] = false;
|
||||
const newCategoricalSelectionState = {
|
||||
...state.categoricalSelectionState,
|
||||
const newCategoricalAsBooleansMap = {
|
||||
...state.categoricalAsBooleansMap,
|
||||
[action.metadataField]: {
|
||||
...state.categoricalSelectionState[action.metadataField],
|
||||
categorySelected: newCategorySelected
|
||||
...state.categoricalAsBooleansMap[action.metadataField],
|
||||
[action.value]: false
|
||||
}
|
||||
};
|
||||
|
||||
// update the filter to match all selected options
|
||||
const cat = newCategoricalSelectionState[action.metadataField];
|
||||
// update the filter for the one category that changed state
|
||||
state.dimensionMap[obsAnnoDimensionName(action.metadataField)].filterEnum(
|
||||
selectedValuesForCategory(cat)
|
||||
_.filter(
|
||||
_.map(
|
||||
newCategoricalAsBooleansMap[action.metadataField],
|
||||
(val, key) => (val ? key : false)
|
||||
)
|
||||
)
|
||||
);
|
||||
|
||||
return {
|
||||
...state,
|
||||
categoricalSelectionState: newCategoricalSelectionState
|
||||
categoricalAsBooleansMap: newCategoricalAsBooleansMap
|
||||
};
|
||||
}
|
||||
case "categorical metadata filter none of these": {
|
||||
const newCategoricalSelectionState = {
|
||||
...state.categoricalSelectionState,
|
||||
[action.metadataField]: {
|
||||
...state.categoricalSelectionState[action.metadataField],
|
||||
categorySelected: Array.from(
|
||||
state.categoricalSelectionState[action.metadataField]
|
||||
.categorySelected
|
||||
).fill(false)
|
||||
}
|
||||
const newCategoricalAsBooleansMap = {
|
||||
...state.categoricalAsBooleansMap
|
||||
};
|
||||
_.forEach(
|
||||
newCategoricalAsBooleansMap[action.metadataField],
|
||||
(v, k, c) => {
|
||||
c[k] = false;
|
||||
}
|
||||
);
|
||||
state.dimensionMap[
|
||||
obsAnnoDimensionName(action.metadataField)
|
||||
].filterNone();
|
||||
return {
|
||||
...state,
|
||||
categoricalSelectionState: newCategoricalSelectionState
|
||||
categoricalAsBooleansMap: newCategoricalAsBooleansMap
|
||||
};
|
||||
}
|
||||
case "categorical metadata filter all of these": {
|
||||
const newCategoricalSelectionState = {
|
||||
...state.categoricalSelectionState,
|
||||
[action.metadataField]: {
|
||||
...state.categoricalSelectionState[action.metadataField],
|
||||
categorySelected: Array.from(
|
||||
state.categoricalSelectionState[action.metadataField]
|
||||
.categorySelected
|
||||
).fill(true)
|
||||
}
|
||||
const newCategoricalAsBooleansMap = {
|
||||
...state.categoricalAsBooleansMap
|
||||
};
|
||||
_.forEach(
|
||||
newCategoricalAsBooleansMap[action.metadataField],
|
||||
(v, k, c) => {
|
||||
c[k] = true;
|
||||
}
|
||||
);
|
||||
state.dimensionMap[
|
||||
obsAnnoDimensionName(action.metadataField)
|
||||
].filterAll();
|
||||
return {
|
||||
...state,
|
||||
categoricalSelectionState: newCategoricalSelectionState
|
||||
categoricalAsBooleansMap: newCategoricalAsBooleansMap
|
||||
};
|
||||
}
|
||||
|
||||
|
||||
@@ -31,18 +31,15 @@ export const doJsonRequest = async url => {
|
||||
const res = await fetch(url, {
|
||||
method: "get",
|
||||
headers: new Headers({
|
||||
"Content-Type": "application/json"
|
||||
"Content-Type": "application/json",
|
||||
"Accept-Encoding": "gzip, deflate, br"
|
||||
})
|
||||
});
|
||||
if (res.ok && res.headers.get("Content-Type") === "application/json") {
|
||||
return res.json();
|
||||
}
|
||||
// else an error
|
||||
let msg = `Unexpected HTTP response ${res.status}, ${res.statusText}`;
|
||||
const body = await res.text();
|
||||
if (body && body.length > 0) {
|
||||
msg = `${msg} -- ${body}`;
|
||||
}
|
||||
const msg = `Unexpected HTTP response ${res.status}, ${res.statusText}`;
|
||||
dispatchNetworkErrorMessageToUser(msg);
|
||||
throw new Error(msg);
|
||||
};
|
||||
|
||||
@@ -17,4 +17,3 @@ exists to support those concepts.
|
||||
export * as Universe from "./universe";
|
||||
export * as World from "./world";
|
||||
export * as kvCache from "./keyvalcache";
|
||||
export * as WorldUtil from "./worldUtil";
|
||||
|
||||
@@ -8,7 +8,6 @@ Value will be an object, containing summary information.
|
||||
|
||||
For continuous annotations (int, float, etc):
|
||||
<annotation_name>: {
|
||||
categorical: false,
|
||||
range {
|
||||
min: <number>,
|
||||
max: <number>
|
||||
@@ -16,14 +15,12 @@ For continuous annotations (int, float, etc):
|
||||
}
|
||||
|
||||
For categorical annotations (boolean, string, category):
|
||||
<annotation_name>: {
|
||||
categorical: true,
|
||||
categories: [ <category1>, <category2>, ... ]
|
||||
categoryCounts: Map {
|
||||
<category1>: <number>,
|
||||
<annotatoin_name>: {
|
||||
options: {
|
||||
<option1>: <number>,
|
||||
...
|
||||
},
|
||||
numCategories: <number>
|
||||
numOptions: <number>
|
||||
}
|
||||
|
||||
Summarize will be returned for BOTH obs and var annotations.
|
||||
@@ -31,19 +28,19 @@ Summarize will be returned for BOTH obs and var annotations.
|
||||
Example:
|
||||
{
|
||||
"Splice_sites_Annotated": {
|
||||
categorical: false,
|
||||
range: {
|
||||
"range": {
|
||||
"min": 26,
|
||||
"max": 1075869
|
||||
}
|
||||
},
|
||||
"Selection": {
|
||||
categorical: true,
|
||||
numCategories, 3,
|
||||
categories: [ "Astrocytes(HEPACAM)", "Endothelial(BSC)", "Unpanned" ],
|
||||
categoryCounts: Map {
|
||||
numOptions, 6,
|
||||
"options": {
|
||||
"Astrocytes(HEPACAM)": 714,
|
||||
"Endothelial(BSC)": 123,
|
||||
"Oligodendrocytes(GC)": 294,
|
||||
"Neurons(Thy1)": 685,
|
||||
"Microglia(CD45)": 1108,
|
||||
"Unpanned": 665
|
||||
}
|
||||
}
|
||||
@@ -52,45 +49,37 @@ Example:
|
||||
NOTE: will not summarize the required 'name' annotation, as that is
|
||||
specified as unique per element.
|
||||
*/
|
||||
function _summarizeAnnotations(_schema, annotations) {
|
||||
const summary = _(_schema) // lodash wrapping: https://lodash.com/docs/4.17.11#lodash
|
||||
function summarizeDimension(schema, annotations) {
|
||||
return _(schema)
|
||||
.filter(v => v.name !== "name")
|
||||
.keyBy("name")
|
||||
.mapValues(anno => {
|
||||
const { name, type } = anno;
|
||||
const continuous = type === "int32" || type === "float32";
|
||||
|
||||
if (continuous) {
|
||||
let min = Number.POSITIVE_INFINITY;
|
||||
let max = Number.NEGATIVE_INFINITY;
|
||||
for (let r = 0; r < annotations.length; r += 1) {
|
||||
const val = Number(annotations[r][name]);
|
||||
min = val < min ? val : min;
|
||||
max = val > max ? val : max;
|
||||
}
|
||||
if (!continuous) {
|
||||
const options = _.countBy(annotations, name);
|
||||
const numOptions = _.size(options);
|
||||
return {
|
||||
categorical: false,
|
||||
range: { min, max }
|
||||
numOptions,
|
||||
options
|
||||
};
|
||||
}
|
||||
|
||||
/* else categorical */
|
||||
const categoryCounts = new Map();
|
||||
for (let r = 0; r < annotations.length; r += 1) {
|
||||
const val = annotations[r][name];
|
||||
let curCount = categoryCounts.get(val);
|
||||
if (curCount === undefined) curCount = 0;
|
||||
categoryCounts.set(val, curCount + 1);
|
||||
if (continuous) {
|
||||
let min = Number.POSITIVE_INFINITY;
|
||||
let max = Number.NEGATIVE_INFINITY;
|
||||
_.forEach(annotations, obs => {
|
||||
const val = Number(obs[name]);
|
||||
min = val < min ? val : min;
|
||||
max = val > max ? val : max;
|
||||
});
|
||||
return { range: { min, max } };
|
||||
}
|
||||
return {
|
||||
categorical: true,
|
||||
categories: [...categoryCounts.keys()],
|
||||
categoryCounts,
|
||||
numCategories: categoryCounts.size
|
||||
};
|
||||
|
||||
throw new Error("incomprehensible schema");
|
||||
})
|
||||
.value();
|
||||
return summary;
|
||||
}
|
||||
|
||||
export default function summarizeAnnotations(
|
||||
@@ -99,7 +88,7 @@ export default function summarizeAnnotations(
|
||||
varAnnotations
|
||||
) {
|
||||
return {
|
||||
obs: _summarizeAnnotations(schema.annotations.obs, obsAnnotations),
|
||||
var: _summarizeAnnotations(schema.annotations.var, varAnnotations)
|
||||
obs: summarizeDimension(schema.annotations.obs, obsAnnotations),
|
||||
var: summarizeDimension(schema.annotations.var, varAnnotations)
|
||||
};
|
||||
}
|
||||
|
||||
@@ -161,32 +161,6 @@ function RESTv02LayoutResponseToInternal(response) {
|
||||
return layout;
|
||||
}
|
||||
|
||||
function reconcileSchemaCategoriesWithSummary(universe) {
|
||||
/*
|
||||
where we treat types as (essentially) categorical metadata, update
|
||||
the schema with data-derived categories (in addition to those in
|
||||
the server declared schema).
|
||||
|
||||
For example, boolean defined fields in the schema do not contain
|
||||
explicit declaration of categories (nor do string fields). In these
|
||||
cases, add a 'categories' field to the schema so it is accessible.
|
||||
*/
|
||||
|
||||
_.forEach(universe.schema.annotations.obs, s => {
|
||||
if (
|
||||
s.type === "string" ||
|
||||
s.type === "boolean" ||
|
||||
s.type === "categorical"
|
||||
) {
|
||||
const categories = _.union(
|
||||
_.get(s, "categories", []),
|
||||
_.get(universe.summary.obs[s.name], "categories", [])
|
||||
);
|
||||
s.categories = categories;
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
export function createUniverseFromRestV02Response(
|
||||
configResponse,
|
||||
schemaResponse,
|
||||
@@ -225,7 +199,6 @@ export function createUniverseFromRestV02Response(
|
||||
universe.varAnnotations
|
||||
);
|
||||
|
||||
reconcileSchemaCategoriesWithSummary(universe);
|
||||
return finalize(universe);
|
||||
}
|
||||
|
||||
|
||||
@@ -1,65 +0,0 @@
|
||||
/* eslint-disable import/prefer-default-export */
|
||||
import _ from "lodash";
|
||||
|
||||
/*
|
||||
Various utility functions operating on World/Universe
|
||||
*/
|
||||
|
||||
/*
|
||||
Count unique category values, binning first by dim1 then by dim2
|
||||
Return:
|
||||
|
||||
Map {
|
||||
dim1_val1: Map {
|
||||
dim2_val1: number,
|
||||
dim2_val2: number,
|
||||
...
|
||||
},
|
||||
...
|
||||
}
|
||||
|
||||
*/
|
||||
function _countCategoryValues2D(dim1, dim2, rows) {
|
||||
const dimMap = new Map();
|
||||
for (let r = 0; r < rows.length; r += 1) {
|
||||
const row = rows[r];
|
||||
const val1 = row[dim1];
|
||||
const val2 = row[dim2];
|
||||
let d2Map = dimMap.get(val1);
|
||||
if (d2Map === undefined) {
|
||||
d2Map = new Map();
|
||||
dimMap.set(val1, d2Map);
|
||||
}
|
||||
let curCount = d2Map.get(val2);
|
||||
if (curCount === undefined) {
|
||||
curCount = 0;
|
||||
}
|
||||
d2Map.set(val2, curCount + 1);
|
||||
}
|
||||
return dimMap;
|
||||
}
|
||||
|
||||
let __worldUtilMemoId__ = 0;
|
||||
function _memoizedId(x) {
|
||||
if (!x.__worldUtilMemoId__) {
|
||||
__worldUtilMemoId__ += 1;
|
||||
x.__worldUtilMemoId__ = __worldUtilMemoId__;
|
||||
}
|
||||
return x.__worldUtilMemoId__;
|
||||
}
|
||||
function _countCategoryValues2DResolver(...args) {
|
||||
const id = args[0] + args[1] + _memoizedId(args[2]);
|
||||
return id;
|
||||
}
|
||||
|
||||
export const countCategoryValues2D = _.memoize(
|
||||
_countCategoryValues2D,
|
||||
_countCategoryValues2DResolver
|
||||
);
|
||||
|
||||
/*
|
||||
Clear any cached data within WorldUtil caches, eg, memoized functions
|
||||
*/
|
||||
export function clearCaches() {
|
||||
countCategoryValues2D.cache.clear();
|
||||
}
|
||||
+17
-14
@@ -75,7 +75,7 @@ For a GET URL query parameter:
|
||||
- Annotation name is encoded as `obs:name` or `var:name`<sup>[2](#endnote-2)</sup>.
|
||||
- Enumerated values (string, categorical, boolean) are encoded as option lists, ie, `var:tissue=lung, obs:tumor=true`
|
||||
- Scalar values (int32, float32) are encoded as ranges, ie, `obs:num_reads=1000,10000` where either min or max may be replaced with an asterisk to indicate a half-open range.
|
||||
- Index filters are not allowed within GET URL query parameter filters
|
||||
- Index filters are not be allowed within GET URL query parameter filters
|
||||
- Logically, filters are ANDed, except for repeated annotation names which are ORed. For example, `?X=A&X=B&Y=1` is evaluated as `((X==A or X==B) and Y==1)`
|
||||
|
||||
Example selection for _lung_ and _heart_ tissue with more than 1000 reads:
|
||||
@@ -505,10 +505,10 @@ Generate differential expression (DE) statistics for two specified subsets of da
|
||||
|
||||
Two modes are provided:
|
||||
|
||||
- `topN`: return top N differentially expressed variables (across all variables)
|
||||
- `varFilter`: return DE for caller-provided variable filter (_future_)
|
||||
- Return top N differentially expressed variables (genes)
|
||||
- Return DE for caller-provided variable filter (future)
|
||||
|
||||
Both modes perform calculations using a subset of observations, where each subset is defined by an observation filter (`set1` and `set2`). These filters must not include a variable filter.
|
||||
Both modes perform calculations using a subset of observations, where each subset is defined by an observation filter (`set1` and `set2`).
|
||||
|
||||
If differential expression is not supported by the server, must return an HTTP 501 response. If, in the view of the server, the request will exceed a reasonable interactive time period, must immediately return HTTP 403 error (error return _before_ attempting computation).
|
||||
|
||||
@@ -568,22 +568,24 @@ If differential expression is not supported by the server, must return an HTTP 5
|
||||
|
||||
**Response body:**
|
||||
|
||||
- For 200 Success, differential expression statistics returned as array of arrays, where each contains the following values:
|
||||
- For 200 Success, differential expression statistics returned as array of arrays sorted by obs index, where each contains the following values:
|
||||
|
||||
- **varIndex**: variable index for the computed results
|
||||
- **logfoldchange**: log fold-change of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group,
|
||||
- **avgDiff**: log fold-change of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group,
|
||||
- **pVal**: unadjusted p-value,
|
||||
- **pValAdj**: adjusted p-value
|
||||
- **pValAdj**: Adjusted p-value, based on bonferroni correction using all genes in the original dataset),
|
||||
- **set1AvgExp:** average expression value for all observations in set 1,
|
||||
- **set2AvgExp**: average expression value for all observations in set 2
|
||||
|
||||
Values ordered as:
|
||||
Statistics are encoded as an array of arrays, with fields ordered as:
|
||||
|
||||
_varIndex_, _logfoldchange_, _pVal_, _pValAdj_
|
||||
_varIndex_, _avgDiff_, _pVal_, _pValAdj_, _set1AvgExp_, _set2AvgExp_
|
||||
|
||||
For example:
|
||||
|
||||
```
|
||||
[
|
||||
[ 1720, 2.4679039, 2.3124478092035228e-175, 4.250279073316075e-172 ]
|
||||
[ 328, -2.569489, 2.655706e-63, 3.642036e-57, 383.393, 583.9 ],
|
||||
// ...
|
||||
]
|
||||
```
|
||||
@@ -614,8 +616,8 @@ POST /diffexp/obs
|
||||
200 - Success
|
||||
{
|
||||
"diffexp": [
|
||||
[ 328, -2.569489, 2.655706e-63, 3.642036e-57 ],
|
||||
// [ varIdx, logfoldchange, pVal, pValAdj ],
|
||||
[ 328, -2.569489, 2.655706e-63, 3.642036e-57, 383.393, 583.9 ],
|
||||
// [ varIdx, avgDiff, pVal, pValAdj, set1AvgExp, set2AvgExp ],
|
||||
// ...
|
||||
]
|
||||
}
|
||||
@@ -688,9 +690,10 @@ Routes:
|
||||
- `GET /schema`
|
||||
- `GET /annotations/obs`
|
||||
- `GET /annotations/var`
|
||||
- `GET /layout/obs` - get the default layout
|
||||
- `GET /layout/obs`
|
||||
- `PUT /data/obs` - request will contain a filter by var `name`
|
||||
- `POST /diffexp/obs` - mode `topN`, typically with a `count` of 10, and two sets defined by an obs index filter (`{ filter: { obs: { index: [...] } } }`)
|
||||
- `POST /diffexp/obs` - mode `topN`, typically with a couple of 10, and two sets defined by an obs index filter (`{ filter: { obs: { index: [...] } } }`)
|
||||
- `PUT /layout/obs` - (_coming soon_) request will contain a filter by obs index
|
||||
|
||||
Requests include the following content negotiation headers:
|
||||
|
||||
|
||||
@@ -1 +0,0 @@
|
||||
theme: jekyll-theme-architect
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 1.1 MiB |
Binary file not shown.
|
Before Width: | Height: | Size: 312 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 285 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 644 KiB |
@@ -1,37 +0,0 @@
|
||||
# cellxgene
|
||||
|
||||
cellxgene is an interactive data explorer for single-cell transcriptomics data designed to handle large datasets (1 million cells or more) and integrate with your favorite analysis tools
|
||||
|
||||
## getting started
|
||||
|
||||
install the package
|
||||
> `> pip install cellxgene`
|
||||
|
||||
preprocess the data for use with cellxgene (optional)
|
||||
> `> cellxgene --prepare dataset.h5ad -o processed.h5ad`
|
||||
|
||||
launch the web app
|
||||
> `> cellxgene --launch processed.h5ad`
|
||||
|
||||
## features
|
||||
|
||||
|
||||
|
||||
### inspiration and collaboration
|
||||
|
||||
We've been heavily inspired by several other related single-cell visualization projects:
|
||||
* [UCSC Cell Browswer](http://cells.ucsc.edu/)
|
||||
* [Cytoscape](http://www.cytoscape.org/)
|
||||
* [Xena](https://xena.ucsc.edu/)
|
||||
* [ASAP](https://asap.epfl.ch/)
|
||||
* [Gene Pattern](http://genepattern-notebook.org/)
|
||||
|
||||
We were inspired by Mike Bostock and the [crossfilter](https://github.com/crossfilter) team for the design of our filtering implementation.
|
||||
|
||||
We have been working closely with the [`scanpy`](https://github.com/theislab/scanpy) team to integrate with their awesome analysis tools. Special thanks to Alex Wolf, Fabian Theis, and the rest of the team for their help during development and for providing an example dataset.
|
||||
|
||||
We are eager to explore integrations with other computational backends such as [`Seurat`](https://github.com/satijalab/seurat) or [`Bioconductor`](https://github.com/Bioconductor)
|
||||
|
||||
### help and contact
|
||||
|
||||
Have questions, suggestions, or comments? You can come hang out with us by joining the [CZI Science Slack](https://join-cziscience-slack.herokuapp.com/) and posting in the `#cellxgene-users` channel. As mentioned above, please submit any feature requests or bugs as [Github issues](https://github.com/chanzuckerberg/cellxgene/issues). We'd love to hear from you!
|
||||
@@ -1,35 +0,0 @@
|
||||
## Creating PR
|
||||
1. Name [username]/branchname
|
||||
1. Branch name should be all lowercase
|
||||
2. Words separated by “-”
|
||||
2. Code should address only one issue ideally, make a separate PR for each task
|
||||
3. Description
|
||||
1. Clear explanation of issues solved
|
||||
2. Describe why and how, when appropriate
|
||||
3. Call out specific areas you want extra attention in review (optional)
|
||||
4. If your PR requires more than one reviewer tag those people in the description or comments and let them know that you specifically require them
|
||||
4. Ensure that the PR updates tests and documentation and adds tests where appropriate
|
||||
5. Use github’s issue keywords when PR is addressing an issue https://help.github.com/articles/closing-issues-using-keywords/
|
||||
6. Tags (add at beginning of title)
|
||||
1. [EASY] - small non-controversial change, easy to review
|
||||
2. [DO NOT MERGE] - PR is in progress, do not merge changes
|
||||
|
||||
## Review
|
||||
1. Assign at least one reviewer to submitted PRs. Reviewers should be selected based on expertise in areas affected by the PR (eg, web UI: Colin), and should include Comp Bio and PM as needed.
|
||||
2. Reviewers should approve or request changes (not just comment) and put general and line level comments where appropriate
|
||||
3. As a PR submitter respond to all comments (eg, comment, commit a change, etc)
|
||||
4. External PRs
|
||||
1. For external PRs or PRs not from our core team, core team should assign a reviewer and make initial contact within 1 business day
|
||||
2. Build code on local environment and run smoke tests
|
||||
|
||||
## Required to Merge
|
||||
1. Travis CI Build passing
|
||||
2. At least one reviewer approved
|
||||
1. Exceptions:
|
||||
1. Release PRs where version is just bumped should not need review
|
||||
2. Complex PRs which touch multiple parts of the codebase should have reviews from all relevant parties
|
||||
3. License and Security checks (SNYK) passing. If their server is down and you didn’t add any new external npm or python packages, merge is OK
|
||||
|
||||
## Merging
|
||||
1. Use "squash and merge" option when merging
|
||||
2. If you resolved conflicts, wait until the build passes to merge
|
||||
+16
-30
@@ -14,47 +14,33 @@ The release process should result in the following side-effects:
|
||||
- Tagged github release
|
||||
- Publication to PyPi
|
||||
|
||||
## Recipe
|
||||
## Process
|
||||
|
||||
Follow these steps to create a release.
|
||||
|
||||
1. Preparation:
|
||||
- python3.6 environment, and a cellxgene clone
|
||||
- install required tools: `pip install -r requirements-dev.txt`
|
||||
- Define the release version number, using [semantic versioning](https://semver.org/),
|
||||
and specifying all three digits (eg, 0.3.0)
|
||||
- Write the release title and release notes and add to
|
||||
[release notes document](https://docs.google.com/document/d/1KnHwkYfhyWO5H8BDcMu7y3ogjvq5Yi4OwpmZ8DB6w0Y/edit)
|
||||
- Define the release version number, using [semantic versioning](https://semver.org/)
|
||||
- Write the release title and release notes
|
||||
2. Create a release branch, eg, `release-version`
|
||||
3. In the release branch:
|
||||
- Run `bumpversion --config-file .bumpversion.cfg [major | minor | patch]`,
|
||||
where you choose major/minor/patch depending on which part of the version
|
||||
is being bumped (eg, 0.2.9->0.3 is minor).
|
||||
- Clean up existing environment using `bin/clean`
|
||||
- Build the JS asserts using `bin/build-client`
|
||||
- run `bumpversion --config-file .bumpversion.cfg [major | minor | patch]`
|
||||
- build the JS asserts using `bin/build-client`
|
||||
4. Commit and push the new branch
|
||||
5. Create a PR for the release.
|
||||
- [optional] As needed, conduct PR review.
|
||||
6. Merge to master
|
||||
7. Create Github release using the version number and release notes
|
||||
([instructions](https://help.github.com/articles/creating-releases/)).
|
||||
- Draft new release
|
||||
- Type version name matching release version number from (1)
|
||||
- Select `master` as release branch (ensure you merged the release PR)
|
||||
- Type title `Release {version num}`
|
||||
- [optional] Check pre-release if this release is not ready for production
|
||||
- Publish Release
|
||||
8. Publish to pypi by performing the following steps (assumes you have `setuptools`
|
||||
and `twine` installed, that you have registered for pypi, and that you have
|
||||
write access to the cellxgene pypi package):
|
||||
- Build the distribution by calling `python setup.py sdist`
|
||||
6. Create Github release using the version number and release notes ([instructions](https://help.github.com/articles/creating-releases/)).
|
||||
7. Publish to pypi by performing the following steps
|
||||
(assumes you have `setuptools` and `twine` installed and that you have
|
||||
registered for pypi and have write access to the cellxgene pypi package)
|
||||
- build the distribution by calling
|
||||
`python setup.py sdist`
|
||||
inside the top-level directory
|
||||
- [optional] Upload the package to test pypi
|
||||
- [optional] upload the package to test pypi
|
||||
`twine upload --repository-url https://test.pypi.org/legacy/ dist/*`
|
||||
- [optional] Test the test installation in a fresh virtual environment using
|
||||
`pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple cellxgene`
|
||||
- Upload the package to real pypi using `twine upload dist/*`
|
||||
- [optional] Test the installation in a fresh virtual environment using
|
||||
- [optional] test the test installation in a fresh virtual environment using
|
||||
`pip install --index-url https://test.pypi.org/simple/ cellxgene`
|
||||
- upload the package to real pypi using `twine upload dist/*`
|
||||
- [optional] test the installation in a fresh virtual environment using
|
||||
`pip install cellxgene`
|
||||
|
||||
The optional steps are for testing purposes, and are recommended
|
||||
|
||||
|
Before Width: | Height: | Size: 6.0 MiB After Width: | Height: | Size: 6.0 MiB |
+2
-5
@@ -2,14 +2,11 @@
|
||||
if __package__ is None:
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
PKG_PATH = Path(__file__).parent
|
||||
sys.path.insert(0, str(PKG_PATH.parent))
|
||||
import server # noqa F401
|
||||
|
||||
import server
|
||||
__package__ = PKG_PATH.name
|
||||
|
||||
# Main thing
|
||||
from .cli.cli import cli # noqa F402
|
||||
|
||||
from .cli.cli import cli
|
||||
cli()
|
||||
|
||||
+6
-10
@@ -14,14 +14,16 @@ REACTIVE_LIMIT = 1_000_000
|
||||
|
||||
app = Flask(__name__, static_folder="web/static")
|
||||
app.json_encoder = Float32JSONEncoder
|
||||
cache = Cache(app, config={"CACHE_TYPE": "simple", "CACHE_DEFAULT_TIMEOUT": 860_000})
|
||||
cache = Cache(app, config={"CACHE_TYPE": "simple", "CACHE_DEFAULT_TIMEOUT": 860000})
|
||||
Compress(app)
|
||||
CORS(app)
|
||||
|
||||
# Config
|
||||
SECRET_KEY = os.environ.get("CXG_SECRET_KEY", default="SparkleAndShine")
|
||||
|
||||
app.config.update(SECRET_KEY=SECRET_KEY)
|
||||
app.config.update(
|
||||
SECRET_KEY=SECRET_KEY,
|
||||
)
|
||||
|
||||
# Application Data
|
||||
data = None
|
||||
@@ -34,13 +36,7 @@ docs.append(resources.get_swagger_doc())
|
||||
app.register_blueprint(webapp.bp)
|
||||
app.register_blueprint(resources.blueprint)
|
||||
app.register_blueprint(
|
||||
get_swagger_blueprint(
|
||||
docs,
|
||||
"/api/swagger",
|
||||
produces=["application/json"],
|
||||
title="cellxgene rest api",
|
||||
description="An API connecting ExpressionMatrix2 clustering algorithm to cellxgene",
|
||||
)
|
||||
)
|
||||
get_swagger_blueprint(docs, "/api/swagger", produces=["application/json"], title="cellxgene rest api",
|
||||
description="An API connecting ExpressionMatrix2 clustering algorithm to cellxgene"))
|
||||
|
||||
app.add_url_rule("/", endpoint="index")
|
||||
|
||||
+11
-10
@@ -11,20 +11,23 @@ Sort order for methods
|
||||
|
||||
|
||||
class CXGDriver(metaclass=ABCMeta):
|
||||
|
||||
def __init__(self, data, args):
|
||||
self.data = self._load_data(data)
|
||||
self.layout_method = args["layout"]
|
||||
self.diffexp_method = args["diffexp"]
|
||||
self.max_category_items = args["max_category_items"]
|
||||
self.diffexp_lfc_cutoff = args["diffexp_lfc_cutoff"]
|
||||
self.cluster = None
|
||||
|
||||
@property
|
||||
def features(self):
|
||||
features = {
|
||||
"cluster": {"available": False},
|
||||
"layout": {"obs": {"available": False}, "var": {"available": False}},
|
||||
"diffexp": {"available": False},
|
||||
"layout": {
|
||||
"obs": {"available": False},
|
||||
"var": {"available": False},
|
||||
},
|
||||
"diffexp": {"available": False}
|
||||
}
|
||||
# TODO - Interactive limit should be generated from the actual available methods see GH issue #94
|
||||
if self.layout_method:
|
||||
@@ -80,18 +83,16 @@ class CXGDriver(metaclass=ABCMeta):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def diffexp_topN(self, obsFilter1, obsFilter2, top_n=None, interactive_limit=None):
|
||||
def diffexp(self, filter1, filter2, top_n=None, interactive_limit=None):
|
||||
"""
|
||||
Computes the top N differentially expressed variables between two observation sets. If mode
|
||||
is "TOP_N", then stats for the top N
|
||||
dataframes
|
||||
Computes the top differentially expressed variables between two observation sets. If dataframes
|
||||
contain a subset of variables, then statistics for all variables will be returned, otherwise
|
||||
only the top N vars will be returned.
|
||||
:param obsFilter1: filter: dictionary with filter params for first set of observations
|
||||
:param obsFilter2: filter: dictionary with filter params for second set of observations
|
||||
:param filter1: filter: dictionary with filter params for first set of observations
|
||||
:param filter2: filter: dictionary with filter params for second set of observations
|
||||
:param top_n: Limit results to top N (Top var mode only)
|
||||
:param interactive_limit: -- don't compute if total # genes in dataframes are larger than this
|
||||
:return: top N genes and corresponding stats
|
||||
:return: top genes, stats and expression values for variables
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
+444
-394
File diff suppressed because it is too large
Load Diff
@@ -1,109 +0,0 @@
|
||||
import numpy as np
|
||||
from scipy import sparse, stats
|
||||
|
||||
|
||||
# Convenience function which handles sparse data
|
||||
def _mean_var_n(X):
|
||||
"""
|
||||
Two-pass variance calculation. Numerically (more) stable
|
||||
than naive methods (and same method used by numpy.var())
|
||||
https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Two-pass
|
||||
"""
|
||||
n = X.shape[0]
|
||||
if sparse.issparse(X):
|
||||
mean = X.mean(axis=0).A1
|
||||
dfm = X - mean
|
||||
sumsq = np.sum(np.multiply(dfm, dfm), axis=0).A1
|
||||
v = sumsq / (n - 1)
|
||||
else:
|
||||
mean = X.mean(axis=0)
|
||||
dfm = X - mean
|
||||
sumsq = np.sum(np.multiply(dfm, dfm), axis=0)
|
||||
v = sumsq / (n - 1)
|
||||
|
||||
return mean, v, n
|
||||
|
||||
|
||||
def diffexp_ttest(adata, maskA, maskB, top_n=8, diffexp_lfc_cutoff=0.01):
|
||||
"""
|
||||
Return differential expression statistics for top N variables.
|
||||
|
||||
Algorithm:
|
||||
- compute log fold change (log2(meanA/meanB))
|
||||
- compute Welch's t-test statistic and pvalue (w/ Bonferroni correction)
|
||||
- return top N abs(logfoldchange) where lfc > diffexp_lfc_cutoff
|
||||
|
||||
If there are not N which meet criteria, augment by removing the logfoldchange
|
||||
threshold requirement.
|
||||
|
||||
Notes on alogrithm:
|
||||
- Welch's ttest provides basic statistics test.
|
||||
https://en.wikipedia.org/wiki/Welch%27s_t-test
|
||||
- p-values adjusted with Bonferroni correction.
|
||||
https://en.wikipedia.org/wiki/Bonferroni_correction
|
||||
|
||||
:param adata: anndata dataframe
|
||||
:param maskA: observation selection mask for set 1
|
||||
:param maskB: observation selection mask for set 2
|
||||
:param top_n: number of variables to return stats for
|
||||
:param diffexp_lfc_cutoff: minimum
|
||||
:return: for top N genes, [ varindex, logfoldchange, pval, pval_adj ]
|
||||
"""
|
||||
|
||||
if top_n > adata.n_obs:
|
||||
top_n = adata.n_obs
|
||||
|
||||
# mean, variance, N - calculate for both selections
|
||||
meanA, vA, nA = _mean_var_n(adata._X[maskA])
|
||||
meanB, vB, nB = _mean_var_n(adata._X[maskB])
|
||||
|
||||
# variance / N
|
||||
vnA = vA / min(nA, nB) # overestimate variance, would normally be nA
|
||||
vnB = vB / min(nA, nB) # overestimate variance, would normally be nB
|
||||
sum_vn = vnA + vnB
|
||||
|
||||
# degrees of freedom for Welch's t-test
|
||||
with np.errstate(divide="ignore", invalid="ignore"):
|
||||
dof = sum_vn ** 2 / (vnA ** 2 / (nA - 1) + vnB ** 2 / (nB - 1))
|
||||
dof[np.isnan(dof)] = 1
|
||||
|
||||
# Welch's t-test score calculation
|
||||
with np.errstate(divide="ignore", invalid="ignore"):
|
||||
tscores = (meanA - meanB) / np.sqrt(sum_vn)
|
||||
tscores[np.isnan(tscores)] = 0
|
||||
|
||||
# p-value
|
||||
pvals = stats.t.sf(np.abs(tscores), dof) * 2
|
||||
pvals_adj = pvals * adata._X.shape[1]
|
||||
pvals_adj[pvals_adj > 1] = 1 # cap adjusted p-value at 1
|
||||
|
||||
# logfoldchanges: log2(meanA / meanB)
|
||||
logfoldchanges = np.log2(np.abs((meanA + 1e-9) / (meanB + 1e-9)))
|
||||
|
||||
# find all with lfc > cutoff
|
||||
lfc_above_cutoff_idx = np.nonzero(np.abs(logfoldchanges) > diffexp_lfc_cutoff)[0]
|
||||
stats_to_sort = np.abs(tscores)
|
||||
|
||||
# derive sort order
|
||||
if lfc_above_cutoff_idx.shape[0] > top_n:
|
||||
# partition top N
|
||||
rel_t_partition = np.argpartition(stats_to_sort[lfc_above_cutoff_idx], -top_n)[-top_n:]
|
||||
t_partition = lfc_above_cutoff_idx[rel_t_partition]
|
||||
# sort the top N partition
|
||||
rel_sort_order = np.argsort(stats_to_sort[t_partition])[::-1]
|
||||
sort_order = t_partition[rel_sort_order]
|
||||
else:
|
||||
# partition and sort top N, ignoring lfc cutoff
|
||||
partition = np.argpartition(stats_to_sort, -top_n)[-top_n:]
|
||||
rel_sort_order = np.argsort(stats_to_sort[partition])[::-1]
|
||||
indices = np.indices(stats_to_sort.shape)[0]
|
||||
sort_order = indices[partition][rel_sort_order]
|
||||
|
||||
# top n slice based upon sort order
|
||||
logfoldchanges_top_n = logfoldchanges[sort_order]
|
||||
pvals_top_n = pvals[sort_order]
|
||||
pvals_adj_top_n = pvals_adj[sort_order]
|
||||
|
||||
# varIndex, logfoldchange, pval, pval_adj
|
||||
result = [[sort_order[i], logfoldchanges_top_n[i], pvals_top_n[i], pvals_adj_top_n[i]] for i in range(top_n)]
|
||||
return result
|
||||
@@ -4,12 +4,11 @@ import numpy as np
|
||||
from pandas import DataFrame
|
||||
from pandas.core.dtypes.dtypes import CategoricalDtype
|
||||
import scanpy.api as sc
|
||||
from scipy import sparse
|
||||
from scipy import stats, sparse
|
||||
|
||||
from server.app.driver.driver import CXGDriver
|
||||
from server.app.util.constants import Axis, DEFAULT_TOP_N
|
||||
from server.app.util.constants import Axis, DEFAULT_TOP_N, DiffExpMode
|
||||
from server.app.util.errors import FilterError, InteractiveError, PrepareError, ScanpyFileError
|
||||
from server.app.scanpy_engine.diffexp import diffexp_ttest
|
||||
|
||||
"""
|
||||
Sort order for methods
|
||||
@@ -22,6 +21,7 @@ Sort order for methods
|
||||
|
||||
|
||||
class ScanpyEngine(CXGDriver):
|
||||
|
||||
def __init__(self, data, args):
|
||||
super().__init__(data, args)
|
||||
self._alias_annotation_names(Axis.OBS, args["obs_names"])
|
||||
@@ -34,10 +34,6 @@ class ScanpyEngine(CXGDriver):
|
||||
self.diffexp_options = ["ttest"]
|
||||
self._create_schema()
|
||||
|
||||
# TODO: temporary work-arounds
|
||||
if args["nan_to_num"]:
|
||||
self._IEEE754_special_values_workaround()
|
||||
|
||||
def _alias_annotation_names(self, axis, name):
|
||||
"""
|
||||
Do all user-specified annotation aliasing.
|
||||
@@ -60,54 +56,40 @@ class ScanpyEngine(CXGDriver):
|
||||
if name not in df_axis.columns:
|
||||
raise KeyError(f"Annotation name {name}, specified in --{ax_name}-name does not exist.")
|
||||
if not df_axis[name].is_unique:
|
||||
raise KeyError(
|
||||
f"Values in -{ax_name}-name must be unique. " "Please prepare data to contain unique values."
|
||||
)
|
||||
raise KeyError(f"Values in -{ax_name}-name must be unique. "
|
||||
"Please prepare data to contain unique values.")
|
||||
# reset index to simple range; alias user-specified annotation to "name"
|
||||
df_axis.reset_index(drop=True, inplace=True)
|
||||
df_axis.rename(inplace=True, columns={name: "name"})
|
||||
else:
|
||||
raise KeyError(f"Annotation name {name}, specified in --{ax_name}_name does not exist.")
|
||||
|
||||
@staticmethod
|
||||
def _can_cast_to_float32(ann):
|
||||
if ann.dtype.kind == "f":
|
||||
if not np.can_cast(ann.dtype, np.float32):
|
||||
warnings.warn(f"Annotation {ann.name} will be converted to 32 bit float and may lose precision.")
|
||||
return True
|
||||
return False
|
||||
|
||||
@staticmethod
|
||||
def _can_cast_to_int32(ann):
|
||||
if ann.dtype.kind in ["i", "u"]:
|
||||
if np.can_cast(ann.dtype, np.int32):
|
||||
return True
|
||||
ii32 = np.iinfo(np.int32)
|
||||
if ann.min() >= ii32.min and ann.max() <= ii32.max:
|
||||
return True
|
||||
return False
|
||||
|
||||
def _create_schema(self):
|
||||
self.schema = {
|
||||
"dataframe": {"nObs": self.cell_count, "nVar": self.gene_count, "type": str(self.data.X.dtype)},
|
||||
"annotations": {"obs": [], "var": []},
|
||||
"dataframe": {
|
||||
"nObs": self.cell_count,
|
||||
"nVar": self.gene_count,
|
||||
"type": str(self.data.X.dtype)
|
||||
},
|
||||
"annotations": {
|
||||
"obs": [],
|
||||
"var": []
|
||||
}
|
||||
}
|
||||
for ax in Axis:
|
||||
curr_axis = getattr(self.data, str(ax))
|
||||
for ann in curr_axis:
|
||||
ann_schema = {"name": ann}
|
||||
dtype = curr_axis[ann].dtype
|
||||
data_kind = dtype.kind
|
||||
|
||||
if self._can_cast_to_float32(curr_axis[ann]):
|
||||
data_kind = curr_axis[ann].dtype.kind
|
||||
if data_kind == "f":
|
||||
ann_schema["type"] = "float32"
|
||||
elif self._can_cast_to_int32(curr_axis[ann]):
|
||||
elif data_kind in ["i", "u"]:
|
||||
ann_schema["type"] = "int32"
|
||||
elif dtype == np.bool_:
|
||||
elif data_kind == "?":
|
||||
ann_schema["type"] = "boolean"
|
||||
elif data_kind == "O" and dtype == "object":
|
||||
elif data_kind == "O" and curr_axis[ann].dtype == "object":
|
||||
ann_schema["type"] = "string"
|
||||
elif data_kind == "O" and dtype == "category":
|
||||
elif data_kind == "O" and curr_axis[ann].dtype == "category":
|
||||
ann_schema["type"] = "categorical"
|
||||
ann_schema["categories"] = curr_axis[ann].dtype.categories.tolist()
|
||||
else:
|
||||
@@ -122,35 +104,61 @@ class ScanpyEngine(CXGDriver):
|
||||
try:
|
||||
result = sc.read(data, cache=True)
|
||||
except ValueError:
|
||||
raise ScanpyFileError(
|
||||
"File must be in the .h5ad format. Please read "
|
||||
"https://github.com/theislab/scanpy_usage/blob/master/170505_seurat/info_h5ad.md to "
|
||||
"learn more about this format. You may be able to convert your file into this format "
|
||||
"using `cellxgene prepare`, please run `cellxgene prepare --help` for more "
|
||||
"information."
|
||||
)
|
||||
raise ScanpyFileError("File must be in the .h5ad format. Please read "
|
||||
"https://github.com/theislab/scanpy_usage/blob/master/170505_seurat/info_h5ad.md to "
|
||||
"learn more about this format. You may be able to convert your file into this format "
|
||||
"using `cellxgene prepare`, please run `cellxgene prepare --help` for more "
|
||||
"information.")
|
||||
except Exception as e:
|
||||
raise ScanpyFileError(
|
||||
f"Error while loading file: {e}, File must be in the .h5ad format, please check "
|
||||
f"that your input and try again."
|
||||
)
|
||||
raise ScanpyFileError(f"Error while loading file: {e}, File must be in the .h5ad format, please check "
|
||||
f"that your input and try again.")
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
def _top_sort(values, sort_order, top_n=None):
|
||||
"""
|
||||
Sorts an iterable in sort order limited by top_n
|
||||
:param values: iterable of values to sort
|
||||
:param sort_order: ndarray order to sort in
|
||||
:param top_n: cutoff number to return
|
||||
:return: values sorted by sort_order limited by top_n
|
||||
"""
|
||||
return values[sort_order][:top_n]
|
||||
|
||||
@staticmethod
|
||||
def _nan_to_one(values):
|
||||
"""
|
||||
Replaces NaN values with 1
|
||||
:param values: numpy ndarray
|
||||
:return: ndarray
|
||||
"""
|
||||
return np.where(np.isnan(values), 1, values)
|
||||
|
||||
@staticmethod
|
||||
def _nan_to_zero(values):
|
||||
"""
|
||||
Replaces NaN values with 0
|
||||
:param values: numpy ndarray
|
||||
:return: ndarray
|
||||
"""
|
||||
return np.where(np.isnan(values), 0, values)
|
||||
|
||||
def _validate_data_types(self):
|
||||
if self.data.X.dtype != "float32":
|
||||
warnings.warn(
|
||||
f"Scanpy data matrix is in {self.data.X.dtype} format not float32. " f"Precision may be truncated."
|
||||
)
|
||||
warnings.warn(f"Scanpy data matrix is in {self.data.X.dtype} format not float32. "
|
||||
f"Precision may be truncated.")
|
||||
for ax in Axis:
|
||||
curr_axis = getattr(self.data, str(ax))
|
||||
for ann in curr_axis:
|
||||
datatype = curr_axis[ann].dtype
|
||||
downcast_map = {"int64": "int32", "uint32": "int32", "uint64": "int32", "float64": "float32"}
|
||||
downcast_map = {"int64": "int32",
|
||||
"uint32": "int32",
|
||||
"uint64": "int32",
|
||||
"float64": "float32",
|
||||
}
|
||||
if datatype in downcast_map:
|
||||
warnings.warn(
|
||||
f"Scanpy annotation {ax}:{ann} is in unsupported format: {datatype}. "
|
||||
f"Data will be downcast to {downcast_map[datatype]}."
|
||||
)
|
||||
warnings.warn(f"Scanpy annotation {ax}:{ann} is in unsupported format: {datatype}. "
|
||||
f"Data will be downcast to {downcast_map[datatype]}.")
|
||||
if isinstance(datatype, CategoricalDtype):
|
||||
category_num = len(curr_axis[ann].dtype.categories)
|
||||
if category_num > 500 and category_num > self.max_category_items:
|
||||
@@ -158,8 +166,7 @@ class ScanpyEngine(CXGDriver):
|
||||
f"{str(ax).title()} annotation '{ann}' has {category_num} categories, this may be "
|
||||
f"cumbersome or slow to display. We recommend setting the "
|
||||
f"--max-category-items option to 500, this will hide categorical "
|
||||
f"annotations with more than 500 categories in the UI"
|
||||
)
|
||||
f"annotations with more than 500 categories in the UI")
|
||||
|
||||
def _validate_data_calculations(self):
|
||||
layout_key = f"X_{self.layout_method}"
|
||||
@@ -171,70 +178,9 @@ class ScanpyEngine(CXGDriver):
|
||||
f" layout may have been computed. The requested layout must be pre-calculated and saved "
|
||||
f"back in the h5ad file. You can run "
|
||||
f"`cellxgene prepare --layout {self.layout_method} <datafile>` "
|
||||
f"to solve this problem. "
|
||||
)
|
||||
f"to solve this problem. ")
|
||||
|
||||
def _IEEE754_special_values_workaround(self):
|
||||
"""
|
||||
TODO: temporary workaround
|
||||
|
||||
Because all floating point data is serialized to JSON, and JSON has no means of representing
|
||||
non-finite, floating point special values (NaN, +/-Infinity, etc), we include this temporary
|
||||
work-around.
|
||||
|
||||
This will likely be removed in the future, contingent upon improved marshalling.
|
||||
|
||||
Where non-finite floating point is present in obs, var or X:
|
||||
* issue a warning to the user that these values will be convert to finite numbers.
|
||||
* set NaN to zero, and Infinities to min/max of the element.
|
||||
"""
|
||||
|
||||
# annotations
|
||||
for ax in Axis:
|
||||
curr_axis = getattr(self.data, str(ax))
|
||||
for ann in curr_axis:
|
||||
dtype = curr_axis[ann].dtype
|
||||
if dtype.kind == "f":
|
||||
finite_idx = np.isfinite(curr_axis[ann])
|
||||
if not finite_idx.all():
|
||||
curr_axis.loc[np.isnan(curr_axis[ann]), ann] = 0
|
||||
curr_axis.loc[np.isneginf(curr_axis[ann]), ann] = curr_axis[ann][finite_idx].min()
|
||||
curr_axis.loc[np.isposinf(curr_axis[ann]), ann] = curr_axis[ann][finite_idx].max()
|
||||
warnings.warn(
|
||||
f"{str(ax).title()} annotation '{ann}' contains floating point NaN or Infinities. "
|
||||
f"These will be converted to finite values."
|
||||
)
|
||||
|
||||
# X
|
||||
non_finite_X_found = False
|
||||
if sparse.issparse(self.data._X):
|
||||
coo = self.data._X.tocoo()
|
||||
finite_idx = np.isfinite(coo.data)
|
||||
if not finite_idx.all():
|
||||
non_finite_X_found = True
|
||||
coo.data[np.isnan(coo.data)] = 0
|
||||
coo.data[np.isneginf(coo.data)] = np.min(coo.data[finite_idx])
|
||||
coo.data[np.isposinf(coo.data)] = np.max(coo.data[finite_idx])
|
||||
coo.eliminate_zeros()
|
||||
_X = coo.asformat(self.data._X.getformat())
|
||||
self.data._X = _X
|
||||
else:
|
||||
_X = self.data._X
|
||||
finite_idx = np.isfinite(_X.flat)
|
||||
if not finite_idx.all():
|
||||
non_finite_X_found = True
|
||||
min_X = _X.flat[finite_idx].min()
|
||||
max_X = _X.flat[finite_idx].max()
|
||||
_X[np.isnan(_X)] = 0
|
||||
_X[np.isneginf(_X)] = min_X
|
||||
_X[np.isposinf(_X)] = max_X
|
||||
|
||||
if non_finite_X_found:
|
||||
warnings.warn(
|
||||
"Dataframe X contains floating point NaN or Infinities. " "These will be converted to finite values."
|
||||
)
|
||||
|
||||
def filter_dataframe(self, filter):
|
||||
def filter_dataframe(self, filter, include_uns=False):
|
||||
"""
|
||||
Filter cells from data and return a subset of the data. They can operate on both obs and var dimension with
|
||||
indexing and filtering by annotation value. Filters are combined with the and operator.
|
||||
@@ -243,67 +189,70 @@ class ScanpyEngine(CXGDriver):
|
||||
https://docs.google.com/document/d/1Fxjp1SKtCk7l8QP9-7KAjGXL0eldi_qEnNT0NmlGzXI/edit#heading=h.8qc9q57amldx
|
||||
|
||||
:param filter: dictionary with filter params
|
||||
:param include_uns: bool, include unstructured annotations
|
||||
:return: View into scanpy object with cells/genes filtered
|
||||
"""
|
||||
if not filter:
|
||||
return self.data
|
||||
obs_selector, var_selector = self._filter_to_mask(filter, use_slices=False)
|
||||
data = self._slice(self.data, obs_selector, var_selector)
|
||||
cells_idx = np.ones((self.cell_count,), dtype=bool)
|
||||
genes_idx = np.ones((self.gene_count,), dtype=bool)
|
||||
if Axis.OBS in filter:
|
||||
if "index" in filter["obs"]:
|
||||
cells_idx = self._filter_index(filter["obs"]["index"], cells_idx, Axis.OBS)
|
||||
if "annotation_value" in filter["obs"]:
|
||||
cells_idx = self._filter_annotation(filter["obs"]["annotation_value"], cells_idx, Axis.OBS)
|
||||
if Axis.VAR in filter:
|
||||
if "index" in filter["var"]:
|
||||
genes_idx = self._filter_index(filter["var"]["index"], genes_idx, Axis.VAR)
|
||||
if "annotation_value" in filter["var"]:
|
||||
genes_idx = self._filter_annotation(filter["var"]["annotation_value"], genes_idx, Axis.VAR)
|
||||
|
||||
data = self._slice(self.data, cells_idx, genes_idx)
|
||||
return data
|
||||
|
||||
@staticmethod
|
||||
def _annotation_filter_to_mask(filter, d_axis, count):
|
||||
mask = np.ones((count,), dtype=bool)
|
||||
def _filter_index(self, filter, index, axis):
|
||||
"""
|
||||
Filter data based on index. ex. [1, 3, [111:200]]
|
||||
:param filter: subset of filter dict for obs/var:index
|
||||
:param index: np logical vector containing true for passing false for failing filter
|
||||
:param axis: string obs or var
|
||||
:return: np logical vector for whether the data passes the filter
|
||||
"""
|
||||
if axis == Axis.OBS:
|
||||
count_ = self.cell_count
|
||||
elif axis == Axis.VAR:
|
||||
count_ = self.gene_count
|
||||
idx_filter = np.zeros((count_,), dtype=bool)
|
||||
for i in filter:
|
||||
if type(i) == list:
|
||||
idx_filter[i[0]:i[1]] = True
|
||||
else:
|
||||
idx_filter[i] = True
|
||||
return np.logical_and(index, idx_filter)
|
||||
|
||||
def _filter_annotation(self, filter, index, axis):
|
||||
"""
|
||||
Filter data based on annotation value
|
||||
:param filter: subset of filter dict for obs/var:annotation_value
|
||||
:param index: np logical vector containing true for passing false for failing filter
|
||||
:param axis: string obs or var
|
||||
:return: np logical vector for whether the data passes the filter
|
||||
"""
|
||||
d_axis = getattr(self.data, axis.value)
|
||||
for v in filter:
|
||||
if d_axis[v["name"]].dtype.name in ["boolean", "category", "object"]:
|
||||
key_idx = np.in1d(getattr(d_axis, v["name"]), v["values"])
|
||||
mask = np.logical_and(mask, key_idx)
|
||||
index = np.logical_and(index, key_idx)
|
||||
else:
|
||||
min_ = v.get("min", None)
|
||||
max_ = v.get("max", None)
|
||||
if min_ is not None:
|
||||
key_idx = (getattr(d_axis, v["name"]) >= min_).ravel()
|
||||
mask = np.logical_and(mask, key_idx)
|
||||
index = np.logical_and(index, key_idx)
|
||||
if max_ is not None:
|
||||
key_idx = (getattr(d_axis, v["name"]) <= max_).ravel()
|
||||
mask = np.logical_and(mask, key_idx)
|
||||
return mask
|
||||
|
||||
@staticmethod
|
||||
def _index_filter_to_mask(filter, count):
|
||||
mask = np.zeros((count,), dtype=bool)
|
||||
for i in filter:
|
||||
if type(i) == list:
|
||||
mask[i[0] : i[1]] = True
|
||||
else:
|
||||
mask[i] = True
|
||||
return mask
|
||||
|
||||
@staticmethod
|
||||
def _axis_filter_to_mask(filter, d_axis, count):
|
||||
mask = np.ones((count,), dtype=bool)
|
||||
if "index" in filter:
|
||||
mask = np.logical_and(mask, ScanpyEngine._index_filter_to_mask(filter["index"], count))
|
||||
if "annotation_value" in filter:
|
||||
mask = np.logical_and(
|
||||
mask, ScanpyEngine._annotation_filter_to_mask(filter["annotation_value"], d_axis, count)
|
||||
)
|
||||
return mask
|
||||
|
||||
def _filter_to_mask(self, filter, use_slices=True):
|
||||
if use_slices:
|
||||
obs_selector = slice(0, self.data.n_obs)
|
||||
var_selector = slice(0, self.data.n_vars)
|
||||
else:
|
||||
obs_selector = None
|
||||
var_selector = None
|
||||
|
||||
if filter is not None:
|
||||
if Axis.OBS in filter:
|
||||
obs_selector = self._axis_filter_to_mask(filter["obs"], self.data.obs, self.data.n_obs)
|
||||
if Axis.VAR in filter:
|
||||
var_selector = self._axis_filter_to_mask(filter["var"], self.data.var, self.data.n_vars)
|
||||
return obs_selector, var_selector
|
||||
index = np.logical_and(index, key_idx)
|
||||
return index
|
||||
|
||||
@staticmethod
|
||||
def _slice(data, obs_selector=None, vars_selector=None):
|
||||
@@ -317,9 +266,8 @@ class ScanpyEngine(CXGDriver):
|
||||
|
||||
https://docs.scipy.org/doc/scipy/reference/sparse.html
|
||||
"""
|
||||
prefer_row_access = (
|
||||
sparse.isspmatrix_csr(data._X) or sparse.isspmatrix_lil(data._X) or sparse.isspmatrix_bsr(data._X)
|
||||
)
|
||||
prefer_row_access = sparse.isspmatrix_csr(data._X) or sparse.isspmatrix_lil(data._X) \
|
||||
or sparse.isspmatrix_bsr(data._X)
|
||||
if prefer_row_access:
|
||||
# Row-major slicing
|
||||
if obs_selector is not None:
|
||||
@@ -345,19 +293,16 @@ class ScanpyEngine(CXGDriver):
|
||||
[observation ids, val1, val2...]
|
||||
"""
|
||||
try:
|
||||
obs_selector, var_selector = self._filter_to_mask(filter)
|
||||
except (KeyError, IndexError) as e:
|
||||
df = self.filter_dataframe(filter)
|
||||
except KeyError as e:
|
||||
raise FilterError(f"Error parsing filter: {e}") from e
|
||||
if axis == Axis.OBS:
|
||||
obs = self.data.obs[obs_selector]
|
||||
if not fields:
|
||||
fields = obs.columns.tolist()
|
||||
result = {"names": fields, "data": DataFrame(obs[fields]).to_records(index=True).tolist()}
|
||||
else:
|
||||
var = self.data.var[var_selector]
|
||||
if not fields:
|
||||
fields = var.columns.tolist()
|
||||
result = {"names": fields, "data": DataFrame(var[fields]).to_records(index=True).tolist()}
|
||||
df_axis = getattr(df, axis)
|
||||
if not fields:
|
||||
fields = df_axis.columns.tolist()
|
||||
result = {
|
||||
"names": fields,
|
||||
"data": DataFrame(df_axis[fields]).to_records(index=True).tolist()
|
||||
}
|
||||
return result
|
||||
|
||||
def data_frame(self, filter, axis):
|
||||
@@ -371,38 +316,85 @@ class ScanpyEngine(CXGDriver):
|
||||
}
|
||||
"""
|
||||
try:
|
||||
obs_selector, var_selector = self._filter_to_mask(filter)
|
||||
except (KeyError, IndexError) as e:
|
||||
slice = self.filter_dataframe(filter)
|
||||
except KeyError as e:
|
||||
raise FilterError(f"Error parsing filter: {e}") from e
|
||||
_X = self.data._X[obs_selector, var_selector]
|
||||
if sparse.issparse(_X):
|
||||
_X = _X.toarray()
|
||||
var_index_sliced = self.data.var.index[var_selector]
|
||||
obs_index_sliced = self.data.obs.index[obs_selector]
|
||||
# convert sparse slice to dense
|
||||
X = slice._X.toarray() if sparse.issparse(slice._X) else slice._X
|
||||
if axis == Axis.OBS:
|
||||
result = {
|
||||
"var": var_index_sliced.tolist(),
|
||||
"obs": DataFrame(_X, index=obs_index_sliced).to_records(index=True).tolist(),
|
||||
"var": slice.var.index.tolist(),
|
||||
"obs": DataFrame(X, index=slice.obs.index).to_records(index=True).tolist()
|
||||
}
|
||||
else:
|
||||
result = {
|
||||
"obs": obs_index_sliced.tolist(),
|
||||
"var": DataFrame(_X.T, index=var_index_sliced).to_records(index=True).tolist(),
|
||||
"obs": slice.obs.index.tolist(),
|
||||
"var": DataFrame(X.T, index=slice.var.index).to_records(index=True).tolist()
|
||||
}
|
||||
return result
|
||||
|
||||
def diffexp_topN(self, obsFilterA, obsFilterB, top_n=None, interactive_limit=None):
|
||||
if Axis.VAR in obsFilterA or Axis.VAR in obsFilterB:
|
||||
raise FilterError("Observation filters may not contain vaiable conditions")
|
||||
def diffexp(self, filter1, filter2, top_n=None, interactive_limit=None):
|
||||
"""
|
||||
Computes the top differentially expressed variables between two observation sets. If dataframes
|
||||
contain a subset of variables, then statistics for all variables will be returned, otherwise
|
||||
only the top N vars will be returned.
|
||||
:param filter1: filter: dictionary with filter params for first set of observations
|
||||
:param filter2: filter: dictionary with filter params for second set of observations
|
||||
:param top_n: Limit results to top N (Top var mode only)
|
||||
:param interactive_limit: -- don't compute if total # genes in dataframes are larger than this
|
||||
:return: top genes, stats and expression values for variables
|
||||
"""
|
||||
try:
|
||||
obs_mask_A = self._axis_filter_to_mask(obsFilterA["obs"], self.data.obs, self.data.n_obs)
|
||||
obs_mask_B = self._axis_filter_to_mask(obsFilterB["obs"], self.data.obs, self.data.n_obs)
|
||||
except (KeyError, IndexError) as e:
|
||||
raise FilterError(f"Error parsing filter: {e}") from e
|
||||
if top_n is None:
|
||||
top_n = DEFAULT_TOP_N
|
||||
result = diffexp_ttest(self.data, obs_mask_A, obs_mask_B, top_n, self.diffexp_lfc_cutoff)
|
||||
return result
|
||||
df1 = self.filter_dataframe(filter1)
|
||||
except KeyError as e:
|
||||
raise FilterError(f"Error parsing filter for set 1: {e}") from e
|
||||
# TODO df2 should be inverse if not filter2 provided
|
||||
try:
|
||||
df2 = self.filter_dataframe(filter2)
|
||||
except KeyError as e:
|
||||
raise FilterError(f"Error parsing filter for set 2: {e}") from e
|
||||
# If not the same genes, test is wrong!
|
||||
if np.any(df1.var.index != df2.var.index):
|
||||
raise ValueError("Variables ares not the same in set1 and set2")
|
||||
if interactive_limit and df1.shape[0] + df2.shape[0] > interactive_limit:
|
||||
raise InteractiveError("Size of set 1 and 2 is too large for interactive computation")
|
||||
# If not all genes, they used a var filter
|
||||
if df1.var.shape[0] < self.gene_count:
|
||||
mode = DiffExpMode.VAR_FILTER
|
||||
if top_n:
|
||||
raise Warning("Top N was specified but will not be used in 'Var Filter' mode")
|
||||
else:
|
||||
mode = DiffExpMode.TOP_N
|
||||
if not top_n:
|
||||
top_n = DEFAULT_TOP_N
|
||||
|
||||
genes_idx = df1.var.index
|
||||
# ensure we are using a dense ndarray
|
||||
X1 = df1._X.toarray() if sparse.issparse(df1._X) else df1._X
|
||||
X2 = df2._X.toarray() if sparse.issparse(df2._X) else df2._X
|
||||
diffexp_result = stats.ttest_ind(X1, X2)
|
||||
tstats = self._nan_to_zero(diffexp_result.statistic)
|
||||
pval = self._nan_to_one(diffexp_result.pvalue)
|
||||
bonferroni_pval = 1 - (1 - pval) ** self.gene_count
|
||||
ave_exp_set1 = np.mean(X1, axis=0)
|
||||
ave_exp_set2 = np.mean(X2, axis=0)
|
||||
ave_diff = ave_exp_set1 - ave_exp_set2
|
||||
if mode == DiffExpMode.TOP_N:
|
||||
sort_order = np.argsort(np.abs(tstats))[::-1]
|
||||
# If top_n > length it will just return length
|
||||
genes = self._top_sort(genes_idx, sort_order, top_n)
|
||||
pval = self._top_sort(pval, sort_order, top_n)
|
||||
bonferroni_pval = self._top_sort(bonferroni_pval, sort_order, top_n)
|
||||
ave_exp_set1 = self._top_sort(ave_exp_set1, sort_order, top_n)
|
||||
ave_exp_set2 = self._top_sort(ave_exp_set2, sort_order, top_n)
|
||||
ave_diff = self._top_sort(ave_diff, sort_order, top_n)
|
||||
|
||||
# varIndex, avgDiff, pVal, pValAdj, set1AvgExp, set2AvgExp
|
||||
result = []
|
||||
for i in range(len(genes)):
|
||||
result.append([genes[i], ave_diff[i], pval[i], bonferroni_pval[i], ave_exp_set1[i], ave_exp_set2[i]])
|
||||
# Results need to be returned in var index order
|
||||
return sorted(result, key=lambda gene: gene[0])
|
||||
|
||||
def layout(self, filter, interactive_limit=None):
|
||||
"""
|
||||
@@ -412,8 +404,8 @@ class ScanpyEngine(CXGDriver):
|
||||
:return: [cellid, x, y, ...]
|
||||
"""
|
||||
try:
|
||||
df = self.filter_dataframe(filter)
|
||||
except (KeyError, IndexError) as e:
|
||||
df = self.filter_dataframe(filter, include_uns=True)
|
||||
except KeyError as e:
|
||||
raise FilterError(f"Error parsing filter: {e}") from e
|
||||
if interactive_limit and len(df.obs.index) > interactive_limit:
|
||||
raise InteractiveError("Size data is too large for interactive computation")
|
||||
@@ -426,11 +418,11 @@ class ScanpyEngine(CXGDriver):
|
||||
try:
|
||||
df_layout = df.obsm[f"X_{self.layout_method}"]
|
||||
except ValueError as e:
|
||||
raise PrepareError(
|
||||
f"Layout has not been calculated using {self.layout_method}, "
|
||||
f"please prepare your datafile and relaunch cellxgene"
|
||||
) from e
|
||||
normalized_layout = DataFrame(
|
||||
(df_layout - df_layout.min()) / (df_layout.max() - df_layout.min()), index=df.obs.index
|
||||
)
|
||||
return {"ndims": normalized_layout.shape[1], "coordinates": normalized_layout.to_records(index=True).tolist()}
|
||||
raise PrepareError(f"Layout has not been calculated using {self.layout_method}, "
|
||||
f"please prepare your datafile and relaunch cellxgene") from e
|
||||
normalized_layout = DataFrame((df_layout - df_layout.min()) / (df_layout.max() - df_layout.min()),
|
||||
index=df.obs.index)
|
||||
return {
|
||||
"ndims": normalized_layout.shape[1],
|
||||
"coordinates": normalized_layout.to_records(index=True).tolist()
|
||||
}
|
||||
|
||||
@@ -25,6 +25,3 @@ class Axis(AugmentedEnum):
|
||||
class DiffExpMode(AugmentedEnum):
|
||||
TOP_N = "topN"
|
||||
VAR_FILTER = "varFilter"
|
||||
|
||||
|
||||
JSON_NaN_to_num_warning_msg = "JSON encoding failure - suggest trying --nan-to-num command line option"
|
||||
|
||||
@@ -7,6 +7,7 @@ from server.app.util.constants import Axis
|
||||
|
||||
|
||||
class QueryStringError(Exception):
|
||||
|
||||
def __init__(self, key, message):
|
||||
self.key = key
|
||||
self.message = message
|
||||
|
||||
@@ -5,13 +5,24 @@ class AnnotationModel(Schema):
|
||||
type = "object"
|
||||
description = "Filter by annotation key: value"
|
||||
properties = {
|
||||
"name": {"type": "string"},
|
||||
"name": {
|
||||
"type": "string"
|
||||
},
|
||||
# TODO update to OpenAPI v3.0 when a library is available that supports it
|
||||
# Unfortunately 2.0 doesn't have a way to have a schema that accepts multiple types
|
||||
# Overloading the type key with a list seems to work ok and makes it to the page
|
||||
"values": {"type": "array", "items": {"type": ["float32", "string", "int32", "bool"]}},
|
||||
"min": {"type": ["int32", "float32"]},
|
||||
"max": {"type": ["int32", "float32"]},
|
||||
"values": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"type": ["float32", "string", "int32", "bool"]
|
||||
}
|
||||
},
|
||||
"min": {
|
||||
"type": ["int32", "float32"],
|
||||
},
|
||||
"max": {
|
||||
"type": ["int32", "float32"],
|
||||
}
|
||||
}
|
||||
required = ["name"]
|
||||
|
||||
@@ -19,16 +30,36 @@ class AnnotationModel(Schema):
|
||||
class IndexModel(Schema):
|
||||
type = "object"
|
||||
description = "Filter by index of observation/variable ex. [0, 5, 15]"
|
||||
properties = {"index": {"type": "array", "items": {"format": "int32", "type": "integer"}}}
|
||||
properties = {
|
||||
"index": {
|
||||
"type": "array",
|
||||
"items": {
|
||||
"format": "int32",
|
||||
"type": "integer"
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
class AxisModel(Schema):
|
||||
type = "object"
|
||||
description = "Axis of data -- obs or var"
|
||||
properties = {"index": IndexModel, "annotation_value": AnnotationModel.array()}
|
||||
properties = {
|
||||
"index": IndexModel,
|
||||
"annotation_value": AnnotationModel.array()
|
||||
}
|
||||
|
||||
|
||||
class FilterModel(Schema):
|
||||
type = "object"
|
||||
description = "Complex filter"
|
||||
properties = {"filter": {"type": "object", "properties": {"obs": AxisModel, "var": AxisModel}}}
|
||||
properties = {
|
||||
"filter": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"obs": AxisModel,
|
||||
"var": AxisModel
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -7,17 +7,6 @@ from server.app.util.errors import MimeTypeError
|
||||
|
||||
|
||||
class Float32JSONEncoder(json.JSONEncoder):
|
||||
def __init__(self, *args, **kwargs):
|
||||
"""
|
||||
NaN/Infinities are illegal in standard JSON. Python extends JSON with
|
||||
non-standard symbols that most JavaScript JSON parsers do not understand.
|
||||
The `allow_nan` parameter will force Python simplejson to throw an ValueError
|
||||
if it runs into non-finite floating point values which are unsupported by
|
||||
standard JSON.
|
||||
"""
|
||||
kwargs["allow_nan"] = False
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def default(self, obj):
|
||||
if isinstance(obj, float32):
|
||||
return float(obj)
|
||||
@@ -26,13 +15,8 @@ class Float32JSONEncoder(json.JSONEncoder):
|
||||
return json.JSONEncoder.default(self, obj)
|
||||
|
||||
|
||||
def custom_format_warning(msg, *args, **kwargs):
|
||||
return f"[cellxgene] Warning: {msg} \n"
|
||||
|
||||
|
||||
def get_mime_type(
|
||||
default="application/json", acceptable_types=["application/json", "text/csv"], query_param=None, header=None
|
||||
):
|
||||
def get_mime_type(default="application/json", acceptable_types=["application/json", "text/csv"], query_param=None,
|
||||
header=None):
|
||||
mime_type = default
|
||||
if query_param:
|
||||
if query_param in acceptable_types:
|
||||
|
||||
@@ -1,5 +1,7 @@
|
||||
import os
|
||||
from flask import Blueprint, render_template, send_from_directory, current_app
|
||||
from flask import (
|
||||
Blueprint, render_template, send_from_directory, current_app
|
||||
)
|
||||
|
||||
|
||||
bp = Blueprint("webapp", __name__, template_folder="templates")
|
||||
|
||||
+1
-1
@@ -5,7 +5,7 @@ from .prepare import prepare
|
||||
|
||||
|
||||
@click.group(name="cellxgene", context_settings=dict(max_content_width=85))
|
||||
@click.version_option(version="0.4.0", prog_name="cellxgene", message="[%(prog)s] Version %(version)s")
|
||||
@click.version_option(version="0.0.2", prog_name="cellxgene", message="[%(prog)s] Version %(version)s")
|
||||
def cli():
|
||||
pass
|
||||
|
||||
|
||||
+33
-88
@@ -1,88 +1,34 @@
|
||||
import logging
|
||||
from os import devnull
|
||||
from os.path import splitext, basename
|
||||
import sys
|
||||
import warnings
|
||||
import click
|
||||
import logging
|
||||
from os.path import splitext, basename
|
||||
import webbrowser
|
||||
|
||||
import click
|
||||
|
||||
from server.app.util.errors import ScanpyFileError
|
||||
from server.app.util.utils import custom_format_warning
|
||||
|
||||
|
||||
@click.command()
|
||||
@click.argument("data", metavar="<data file>", type=click.Path(exists=True, file_okay=True, dir_okay=False))
|
||||
@click.option(
|
||||
"--layout", "-l", type=click.Choice(["umap", "tsne"]), default="umap", show_default=True, help="Method for layout."
|
||||
)
|
||||
@click.option(
|
||||
"--diffexp",
|
||||
"-d",
|
||||
type=click.Choice(["ttest"]),
|
||||
default="ttest",
|
||||
show_default=True,
|
||||
help="Method for differential expression.",
|
||||
)
|
||||
@click.option("--layout", "-l", type=click.Choice(["umap", "tsne"]), default="umap", show_default=True,
|
||||
help="Method for layout.")
|
||||
@click.option("--diffexp", "-d", type=click.Choice(["ttest"]), default="ttest", show_default=True,
|
||||
help="Method for differential expression.")
|
||||
@click.option("--title", "-t", help="Title to display (if omitted will use file name).", metavar="")
|
||||
@click.option(
|
||||
"--verbose",
|
||||
"-v",
|
||||
is_flag=True,
|
||||
default=False,
|
||||
show_default=True,
|
||||
help="Provide verbose output, including warnings and all server requests.",
|
||||
)
|
||||
@click.option("--debug", "-d", is_flag=True, default=False, show_default=True, help="Run in debug mode.")
|
||||
@click.option(
|
||||
"--open",
|
||||
"-o",
|
||||
"open_browser",
|
||||
is_flag=True,
|
||||
default=False,
|
||||
show_default=True,
|
||||
help="Open the web browser after launch.",
|
||||
)
|
||||
@click.option("--verbose", "-v", is_flag=True, default=False, show_default=True,
|
||||
help="Provide verbose output, including warnings and all server requests.")
|
||||
@click.option("--debug", "-d", is_flag=True, default=False, show_default=True,
|
||||
help="Run in debug mode.")
|
||||
@click.option("--open", "-o", "open_browser", is_flag=True, default=False, show_default=True,
|
||||
help="Open the web browser after launch.")
|
||||
@click.option("--port", "-p", help="Port to run server on.", metavar="", default=5005, show_default=True)
|
||||
@click.option("--obs-names", default=None, metavar="", help="Name of annotation field to use for observations.")
|
||||
@click.option("--var-names", default=None, metavar="", help="Name of annotation to use for variables.")
|
||||
@click.option("--host", default="127.0.0.1", help="Host IP address")
|
||||
@click.option(
|
||||
"--max-category-items",
|
||||
default=100,
|
||||
metavar="",
|
||||
show_default=True,
|
||||
help="Limits the number of categorical annotation items displayed.",
|
||||
)
|
||||
@click.option(
|
||||
"--diffexp-lfc-cutoff",
|
||||
default=0.01,
|
||||
show_default=True,
|
||||
help="Relative expression cutoff used when selecting top N differentially expressed genes",
|
||||
)
|
||||
@click.option(
|
||||
"--nan-to-num",
|
||||
is_flag=True,
|
||||
default=False,
|
||||
show_default=True,
|
||||
help="Replace all floating point NaN with zero, and infinities with finite numbers",
|
||||
)
|
||||
def launch(
|
||||
data,
|
||||
layout,
|
||||
diffexp,
|
||||
title,
|
||||
verbose,
|
||||
debug,
|
||||
obs_names,
|
||||
var_names,
|
||||
open_browser,
|
||||
port,
|
||||
host,
|
||||
max_category_items,
|
||||
diffexp_lfc_cutoff,
|
||||
nan_to_num,
|
||||
):
|
||||
@click.option("--listen-all", is_flag=True, default=False, show_default=True,
|
||||
help="Bind to all interfaces (this makes the server accessible beyond this computer).")
|
||||
@click.option("--max-category-items", default=100, metavar="", show_default=True,
|
||||
help="Limits the number of categorical annotation items displayed.")
|
||||
def launch(data, layout, diffexp, title, verbose, debug, obs_names, var_names,
|
||||
open_browser, port, listen_all, max_category_items):
|
||||
"""Launch the cellxgene data viewer.
|
||||
This web app lets you explore single-cell expression data.
|
||||
Data must be in a format that cellxgene expects, read the
|
||||
@@ -97,6 +43,9 @@ def launch(
|
||||
# Startup message
|
||||
click.echo("[cellxgene] Starting the CLI...")
|
||||
|
||||
# Import Flask app
|
||||
from server.app.app import app
|
||||
|
||||
# Argument checking
|
||||
name, extension = splitext(data)
|
||||
if extension != ".h5ad":
|
||||
@@ -105,8 +54,6 @@ def launch(
|
||||
if debug:
|
||||
verbose = True
|
||||
open_browser = False
|
||||
else:
|
||||
warnings.formatwarning = custom_format_warning
|
||||
|
||||
if not verbose:
|
||||
sys.tracebacklimit = 0
|
||||
@@ -115,14 +62,19 @@ def launch(
|
||||
file_parts = splitext(basename(data))
|
||||
title = file_parts[0]
|
||||
|
||||
if listen_all:
|
||||
host = "0.0.0.0"
|
||||
else:
|
||||
host = "127.0.0.1"
|
||||
|
||||
# Setup app
|
||||
cellxgene_url = f"http://{host}:{port}"
|
||||
api_base = f"{cellxgene_url}/api/"
|
||||
|
||||
# Import Flask app
|
||||
from server.app.app import app
|
||||
|
||||
app.config.update(DATASET_TITLE=title, CXG_API_BASE=api_base)
|
||||
app.config.update(
|
||||
DATASET_TITLE=title,
|
||||
CXG_API_BASE=api_base
|
||||
)
|
||||
|
||||
if not verbose:
|
||||
log = logging.getLogger("werkzeug")
|
||||
@@ -133,18 +85,15 @@ def launch(
|
||||
# Fix for anaconda python. matplotlib typically expects python to be installed as a framework TKAgg is usually
|
||||
# available and fixes this issue. See https://matplotlib.org/faq/virtualenv_faq.html
|
||||
import matplotlib as mpl
|
||||
|
||||
mpl.use("TkAgg")
|
||||
mpl.use('TkAgg')
|
||||
from server.app.scanpy_engine.scanpy_engine import ScanpyEngine
|
||||
|
||||
args = {
|
||||
"layout": layout,
|
||||
"diffexp": diffexp,
|
||||
"max_category_items": max_category_items,
|
||||
"diffexp_lfc_cutoff": diffexp_lfc_cutoff,
|
||||
"obs_names": obs_names,
|
||||
"var_names": var_names,
|
||||
"nan_to_num": nan_to_num,
|
||||
"var_names": var_names
|
||||
}
|
||||
|
||||
try:
|
||||
@@ -160,8 +109,4 @@ def launch(
|
||||
|
||||
click.echo("[cellxgene] Type CTRL-C at any time to exit.")
|
||||
|
||||
if not verbose:
|
||||
f = open(devnull, "w")
|
||||
sys.stdout = f
|
||||
|
||||
app.run(host=host, debug=debug, port=port, threaded=True)
|
||||
|
||||
+17
-48
@@ -7,48 +7,22 @@ from scipy.sparse.csc import csc_matrix
|
||||
|
||||
@click.command()
|
||||
@click.argument("data", nargs=1, metavar="<dataset: file or path to data>", required=True)
|
||||
@click.option(
|
||||
"--layout",
|
||||
"-l",
|
||||
default=["umap", "tsne"],
|
||||
multiple=True,
|
||||
type=click.Choice(["umap", "tsne"]),
|
||||
help="Layout algorithm",
|
||||
show_default=True,
|
||||
)
|
||||
@click.option(
|
||||
"--recipe",
|
||||
"-r",
|
||||
default="none",
|
||||
type=click.Choice(["none", "seurat", "zheng17"]),
|
||||
help="Preprocessing to run.",
|
||||
show_default=True,
|
||||
)
|
||||
@click.option("--layout", "-l", default=["umap", "tsne"], multiple=True, type=click.Choice(["umap", "tsne"]),
|
||||
help="Layout algorithm", show_default=True)
|
||||
@click.option("--recipe", "-r", default="none", type=click.Choice(["none", "seurat", "zheng17"]),
|
||||
help="Preprocessing to run.", show_default=True)
|
||||
@click.option("--output", "-o", default="", help="Save a new file to filename.", metavar="<filename>")
|
||||
@click.option("--plotting", "-p", default=False, is_flag=True, help="Whether to generate plots.", show_default=True)
|
||||
@click.option("--sparse", default=False, is_flag=True, help="Whether to force sparsity.", show_default=True)
|
||||
@click.option("--overwrite", default=False, is_flag=True, help="Allow file overwriting.", show_default=True)
|
||||
@click.option("--set-obs-names", default="", help="Named field to set as index for obs.", metavar="<name>")
|
||||
@click.option("--set-var-names", default="", help="Named field to set as index for var.", metavar="<name>")
|
||||
@click.option(
|
||||
"--make-obs-names-unique", default=True, is_flag=True, help="Ensure obs index is unique.", show_default=True
|
||||
)
|
||||
@click.option(
|
||||
"--make-var-names-unique", default=True, is_flag=True, help="Ensure var index is unique.", show_default=True
|
||||
)
|
||||
def prepare(
|
||||
data,
|
||||
layout,
|
||||
recipe,
|
||||
output,
|
||||
plotting,
|
||||
sparse,
|
||||
overwrite,
|
||||
set_obs_names,
|
||||
set_var_names,
|
||||
make_obs_names_unique,
|
||||
make_var_names_unique,
|
||||
):
|
||||
@click.option("--make-obs-names-unique", default=True, is_flag=True,
|
||||
help="Ensure obs index is unique.", show_default=True)
|
||||
@click.option("--make-var-names-unique", default=True, is_flag=True,
|
||||
help="Ensure var index is unique.", show_default=True)
|
||||
def prepare(data, layout, recipe, output, plotting, sparse, overwrite,
|
||||
set_obs_names, set_var_names, make_obs_names_unique, make_var_names_unique):
|
||||
"""Preprocesses data for use with cellxgene.
|
||||
|
||||
This tool runs a series of scanpy routines for preparing a dataset
|
||||
@@ -61,7 +35,6 @@ def prepare(
|
||||
# collect slow imports here to make CLI startup more responsive
|
||||
click.echo("[cellxgene] Starting CLI...")
|
||||
import matplotlib
|
||||
|
||||
matplotlib.use("Agg")
|
||||
import scanpy.api as sc
|
||||
|
||||
@@ -76,10 +49,8 @@ def prepare(
|
||||
output = expanduser(output)
|
||||
|
||||
if not output:
|
||||
click.echo(
|
||||
"Warning: No file will be saved, to save the results of cellxgene prepare include "
|
||||
"--output <filename> to save output to a new file"
|
||||
)
|
||||
click.echo("Warning: No file will be saved, to save the results of cellxgene prepare include "
|
||||
"--output <filename> to save output to a new file")
|
||||
if isfile(output) and not overwrite:
|
||||
raise click.UsageError(f"Cannot overwrite existing file {output}, try using the flag --overwrite")
|
||||
|
||||
@@ -148,11 +119,9 @@ def prepare(
|
||||
try:
|
||||
sc.tl.louvain(adata)
|
||||
except ModuleNotFoundError:
|
||||
click.echo(
|
||||
"\nWarning: louvain module is not installed, no clusters will be calculated. "
|
||||
"To fix this please install cellxgene with the optional feature louvain enabled: "
|
||||
"`pip install cellxgene[louvain]`"
|
||||
)
|
||||
click.echo("\nWarning: louvain module is not installed, no clusters will be calculated. "
|
||||
"To fix this please install cellxgene with the optional feature louvain enabled: "
|
||||
"`pip install cellxgene[louvain]`")
|
||||
|
||||
def run_layout(adata):
|
||||
if len(unique(adata.obs["louvain"].values)) < 10:
|
||||
@@ -173,11 +142,11 @@ def prepare(
|
||||
def show_step(item):
|
||||
names = {
|
||||
"make_sparse": "Ensuring sparsity",
|
||||
"run_recipe": f'Running preprocessing recipe "{recipe}"',
|
||||
"run_recipe": f"Running preprocessing recipe \"{recipe}\"",
|
||||
"run_pca": "Running PCA",
|
||||
"run_neighbors": "Calculating neighbors",
|
||||
"run_louvain": "Calculating clusters",
|
||||
"run_layout": "Computing layout",
|
||||
"run_layout": "Computing layout"
|
||||
}
|
||||
if item is not None:
|
||||
return names[item.__name__]
|
||||
|
||||
@@ -1,6 +1,5 @@
|
||||
black
|
||||
bumpversion>=0.5
|
||||
pytest>=3.6.3
|
||||
requests>=2.18.4
|
||||
twine>=1.12.1
|
||||
bumpversion>=0.5
|
||||
-r requirements.txt
|
||||
|
||||
@@ -1,4 +1,4 @@
|
||||
anndata>=0.6.13
|
||||
anndata>=0.6.12
|
||||
click>=6.7
|
||||
Flask>=1.0.2
|
||||
Flask-Caching>=1.4.0
|
||||
@@ -11,4 +11,4 @@ numpy>=1.14.5
|
||||
pandas>=0.23.1
|
||||
scanpy>=1.3.2
|
||||
scipy>=1.1.0
|
||||
scikit-learn>=0.19.1,!=0.20.0
|
||||
scikit-learn==0.19.1
|
||||
|
||||
+80
-60
@@ -9,7 +9,15 @@ LOCAL_URL = "http://127.0.0.1:5005/"
|
||||
VERSION = "v0.2"
|
||||
URL_BASE = f"{LOCAL_URL}api/{VERSION}/"
|
||||
|
||||
BAD_FILTER = {"filter": {"obs": {"annotation_value": [{"name": "xyz"}]}}}
|
||||
BAD_FILTER = {
|
||||
"filter": {
|
||||
"obs": {
|
||||
"annotation_value": [
|
||||
{"name": "xyz"},
|
||||
],
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
class EndPoints(unittest.TestCase):
|
||||
@@ -125,7 +133,7 @@ class EndPoints(unittest.TestCase):
|
||||
{"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
|
||||
{"name": "n_counts", "min": 3000},
|
||||
],
|
||||
"index": [1, 99, [1000, 2000]],
|
||||
"index": [1, 99, [1000, 2000]]
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -146,7 +154,7 @@ class EndPoints(unittest.TestCase):
|
||||
{"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
|
||||
{"name": "n_counts", "min": 3000},
|
||||
],
|
||||
"index": [1, 99, [1000, 2000]],
|
||||
"index": [1, 99, [1000, 2000]]
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -162,9 +170,23 @@ class EndPoints(unittest.TestCase):
|
||||
url = f"{URL_BASE}{endpoint}"
|
||||
params = {
|
||||
"mode": "topN",
|
||||
"set1": {"filter": {"obs": {"annotation_value": [{"name": "louvain", "values": ["NK cells"]}]}}},
|
||||
"set2": {"filter": {"obs": {"annotation_value": [{"name": "louvain", "values": ["CD8 T cells"]}]}}},
|
||||
"count": 7,
|
||||
"set1": {
|
||||
"filter": {
|
||||
"obs": {"annotation_value": [
|
||||
{"name": "louvain", "values": ["NK cells"]}
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"set2": {
|
||||
"filter": {
|
||||
"obs": {"annotation_value": [
|
||||
{"name": "louvain", "values": ["CD8 T cells"]}
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"count": 7
|
||||
}
|
||||
result = self.session.post(url, json=params)
|
||||
self.assertEqual(result.status_code, HTTPStatus.OK)
|
||||
@@ -176,9 +198,20 @@ class EndPoints(unittest.TestCase):
|
||||
url = f"{URL_BASE}{endpoint}"
|
||||
params = {
|
||||
"mode": "topN",
|
||||
"count": 10,
|
||||
"set1": {"filter": {"obs": {"index": [[0, 500]]}}},
|
||||
"set2": {"filter": {"obs": {"index": [[500, 1000]]}}},
|
||||
"set1": {
|
||||
"filter": {
|
||||
"obs": {
|
||||
"index": [[0, 500]]
|
||||
}
|
||||
}
|
||||
},
|
||||
"set2": {
|
||||
"filter": {
|
||||
"obs": {
|
||||
"index": [[500, 1000]]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
result = self.session.post(url, json=params)
|
||||
self.assertEqual(result.status_code, HTTPStatus.OK)
|
||||
@@ -215,7 +248,15 @@ class EndPoints(unittest.TestCase):
|
||||
def test_put_annotations_var(self):
|
||||
endpoint = "annotations/var"
|
||||
url = f"{URL_BASE}{endpoint}"
|
||||
var_filter = {"filter": {"var": {"annotation_value": [{"name": "name", "values": ["ATAD3C", "RER1"]}]}}}
|
||||
var_filter = {
|
||||
"filter": {
|
||||
"var": {
|
||||
"annotation_value": [
|
||||
{"name": "name", "values": ["ATAD3C", "RER1"]},
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
result = self.session.put(url, json=var_filter)
|
||||
self.assertEqual(result.status_code, HTTPStatus.OK)
|
||||
result_data = result.json()
|
||||
@@ -226,7 +267,15 @@ class EndPoints(unittest.TestCase):
|
||||
endpoint = "annotations/var"
|
||||
query = "annotation-name=n_cells"
|
||||
url = f"{URL_BASE}{endpoint}?{query}"
|
||||
var_filter = {"filter": {"var": {"annotation_value": [{"name": "name", "values": ["ATAD3C", "RER1"]}]}}}
|
||||
var_filter = {
|
||||
"filter": {
|
||||
"var": {
|
||||
"annotation_value": [
|
||||
{"name": "name", "values": ["ATAD3C", "RER1"]},
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
result = self.session.put(url, json=var_filter)
|
||||
self.assertEqual(result.status_code, HTTPStatus.OK)
|
||||
result_data = result.json()
|
||||
@@ -285,7 +334,7 @@ class EndPoints(unittest.TestCase):
|
||||
{"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
|
||||
{"name": "n_counts", "min": 3000},
|
||||
],
|
||||
"index": [1, 99, [1000, 2000]],
|
||||
"index": [1, 99, [1000, 2000]]
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -299,7 +348,15 @@ class EndPoints(unittest.TestCase):
|
||||
endpoint = f"data/{axis}"
|
||||
url = f"{URL_BASE}{endpoint}"
|
||||
header = {"Accept": "application/json"}
|
||||
var_filter = {"filter": {"var": {"annotation_value": [{"name": "name", "values": ["RER1"]}]}}}
|
||||
var_filter = {
|
||||
"filter": {
|
||||
"var": {
|
||||
"annotation_value": [
|
||||
{"name": "name", "values": ["RER1"]},
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
result = self.session.put(url, headers=header, json=var_filter)
|
||||
self.assertEqual(result.status_code, HTTPStatus.OK)
|
||||
result_data = result.json()
|
||||
@@ -313,44 +370,16 @@ class EndPoints(unittest.TestCase):
|
||||
def test_cache(self):
|
||||
endpoint = "annotations/var"
|
||||
url = f"{URL_BASE}{endpoint}"
|
||||
f1 = {
|
||||
"filter": {
|
||||
"var": {
|
||||
"annotation_value": [
|
||||
{
|
||||
"name": "name",
|
||||
"values": [
|
||||
"HLA-DRB1",
|
||||
"HLA-DQA1",
|
||||
"HLA-DQB1",
|
||||
"HLA-DPA1",
|
||||
"HLA-DPB1",
|
||||
"MS4A1",
|
||||
"IL32",
|
||||
"CCL5",
|
||||
"CD79B",
|
||||
"CD79A",
|
||||
],
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
f1 = {"filter": {"var": {"annotation_value": [{"name": "name",
|
||||
"values": ["HLA-DRB1", "HLA-DQA1", "HLA-DQB1", "HLA-DPA1",
|
||||
"HLA-DPB1", "MS4A1", "IL32", "CCL5", "CD79B",
|
||||
"CD79A"]}]}}}
|
||||
result = self.session.put(url, json=f1)
|
||||
self.assertEqual(result.status_code, HTTPStatus.OK)
|
||||
result_data1 = result.json()
|
||||
f2 = {
|
||||
"filter": {
|
||||
"var": {
|
||||
"annotation_value": [
|
||||
{
|
||||
"name": "name",
|
||||
"values": ["FGFBP2", "GZMA", "LTB", "PRF1", "CTSW", "GZMH", "CCL5", "CCL4", "CST7", "NKG7"],
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
f2 = {"filter": {"var": {"annotation_value": [{"name": "name",
|
||||
"values": ["FGFBP2", "GZMA", "LTB", "PRF1", "CTSW", "GZMH",
|
||||
"CCL5", "CCL4", "CST7", "NKG7"]}]}}}
|
||||
result = self.session.put(url, json=f2)
|
||||
self.assertEqual(result.status_code, HTTPStatus.OK)
|
||||
result_data2 = result.json()
|
||||
@@ -363,18 +392,9 @@ class EndPoints(unittest.TestCase):
|
||||
result = self.session.put(url, json=f1)
|
||||
self.assertEqual(result.status_code, HTTPStatus.OK)
|
||||
result_data1 = result.json()
|
||||
f2 = {
|
||||
"filter": {
|
||||
"var": {
|
||||
"annotation_value": [
|
||||
{
|
||||
"name": "name",
|
||||
"values": ["FGFBP2", "GZMA", "LTB", "PRF1", "CTSW", "GZMH", "CCL5", "CCL4", "CST7", "NKG7"],
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
f2 = {"filter": {"var": {"annotation_value": [{"name": "name",
|
||||
"values": ["FGFBP2", "GZMA", "LTB", "PRF1", "CTSW", "GZMH",
|
||||
"CCL5", "CCL4", "CST7", "NKG7"]}]}}}
|
||||
result = self.session.put(url, json=f2)
|
||||
self.assertEqual(result.status_code, HTTPStatus.OK)
|
||||
result_data2 = result.json()
|
||||
|
||||
@@ -54,17 +54,13 @@ class UtilTest(unittest.TestCase):
|
||||
|
||||
def test_complex_filter(self):
|
||||
filter_dict = ImmutableMultiDict(
|
||||
[("obs:louvain", "NK cells"), ("obs:louvain", "CD8 T cells"), ("obs:n_counts", "3000,*")]
|
||||
)
|
||||
[("obs:louvain", "NK cells"), ("obs:louvain", "CD8 T cells"), ("obs:n_counts", "3000,*")])
|
||||
filter_ = parse_filter(filter_dict, self.schema)
|
||||
self.assertIn("obs", filter_)
|
||||
self.assertEqual(
|
||||
filter_["obs"]["annotation_value"],
|
||||
[
|
||||
{"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
|
||||
{"name": "n_counts", "max": None, "min": 3000.0},
|
||||
],
|
||||
)
|
||||
self.assertEqual(filter_["obs"]["annotation_value"], [{"name": "louvain",
|
||||
"values": ["NK cells", "CD8 T cells"]},
|
||||
{"name": "n_counts",
|
||||
"max": None, "min": 3000.0}])
|
||||
|
||||
def test_bad_filter(self):
|
||||
bad_annotation_type = ImmutableMultiDict([("obs:tissue", "lung")])
|
||||
@@ -75,7 +71,9 @@ class UtilTest(unittest.TestCase):
|
||||
parse_filter(bad_axis, self.schema)
|
||||
|
||||
def test_boolean_filter(self):
|
||||
schema = {"obs": [{"name": "bool_filter", "type": "boolean"}]}
|
||||
schema = {
|
||||
"obs": [{"name": "bool_filter", "type": "boolean"}]
|
||||
}
|
||||
filter_dict = ImmutableMultiDict([("obs:bool_filter", "false")])
|
||||
filter_ = parse_filter(filter_dict, schema)
|
||||
self.assertIn("obs", filter_)
|
||||
|
||||
@@ -3,6 +3,7 @@ from os import path
|
||||
import pytest
|
||||
import time
|
||||
import unittest
|
||||
import argparse
|
||||
|
||||
import numpy as np
|
||||
from pandas import Series
|
||||
@@ -12,15 +13,8 @@ from server.app.scanpy_engine.scanpy_engine import ScanpyEngine
|
||||
|
||||
class UtilTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
args = {
|
||||
"layout": "umap",
|
||||
"diffexp": "ttest",
|
||||
"max_category_items": 100,
|
||||
"obs_names": None,
|
||||
"var_names": None,
|
||||
"diffexp_lfc_cutoff": 0.01,
|
||||
"nan_to_num": True,
|
||||
}
|
||||
args = {'layout': 'umap', 'diffexp': 'ttest', 'max_category_items': 100,
|
||||
'obs_names': None, 'var_names': None}
|
||||
|
||||
self.data = ScanpyEngine("example-dataset/pbmc3k.h5ad", args)
|
||||
self.data._create_schema()
|
||||
@@ -28,8 +22,8 @@ class UtilTest(unittest.TestCase):
|
||||
def test_init(self):
|
||||
self.assertEqual(self.data.cell_count, 2638)
|
||||
self.assertEqual(self.data.gene_count, 1838)
|
||||
epsilon = 0.000_005
|
||||
self.assertTrue(self.data.data.X[0, 0] - -0.171_469_51 < epsilon)
|
||||
epsilon = 0.000005
|
||||
self.assertTrue(self.data.data.X[0, 0] - -0.17146951 < epsilon)
|
||||
|
||||
def test_mandatory_annotations(self):
|
||||
self.assertIn("name", self.data.data.obs)
|
||||
@@ -44,36 +38,69 @@ class UtilTest(unittest.TestCase):
|
||||
self.data._validate_data_types()
|
||||
|
||||
def test_filter_idx(self):
|
||||
filter_ = {"filter": {"var": {"index": [1, 99, [200, 300]]}, "obs": {"index": [1, 99, [1000, 2000]]}}}
|
||||
filter_ = {
|
||||
"filter": {
|
||||
"var": {
|
||||
"index": [1, 99, [200, 300]]
|
||||
},
|
||||
"obs": {
|
||||
"index": [1, 99, [1000, 2000]]
|
||||
}
|
||||
}
|
||||
}
|
||||
data = self.data.filter_dataframe(filter_["filter"])
|
||||
self.assertEqual(data.shape, (1002, 102))
|
||||
|
||||
def test_filter_annotation(self):
|
||||
filter_ = {
|
||||
"filter": {"obs": {"annotation_value": [{"name": "louvain", "values": ["NK cells", "CD8 T cells"]}]}}
|
||||
"filter": {
|
||||
"obs": {
|
||||
"annotation_value": [
|
||||
{"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
data = self.data.filter_dataframe(filter_["filter"])
|
||||
self.assertEqual(data.shape, (470, 1838))
|
||||
filter_ = {"filter": {"obs": {"annotation_value": [{"name": "n_counts", "min": 3000}]}}}
|
||||
filter_ = {
|
||||
"filter": {
|
||||
"obs": {
|
||||
"annotation_value": [
|
||||
{"name": "n_counts", "min": 3000},
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
data = self.data.filter_dataframe(filter_["filter"])
|
||||
self.assertEqual(data.shape, (497, 1838))
|
||||
|
||||
def test_filter_annotation_no_uns(self):
|
||||
filter_ = {"filter": {"var": {"annotation_value": [{"name": "name", "values": ["RER1"]}]}}}
|
||||
data = self.data.filter_dataframe(filter_["filter"])
|
||||
filter_ = {
|
||||
"filter": {
|
||||
"var": {
|
||||
"annotation_value": [
|
||||
{"name": "name", "values": ["RER1"]},
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
data = self.data.filter_dataframe(filter_["filter"], include_uns=False)
|
||||
self.assertEqual(data.shape[1], 1)
|
||||
|
||||
def test_filter_complex(self):
|
||||
filter_ = {
|
||||
"filter": {
|
||||
"var": {"index": [1, 99, [200, 300]]},
|
||||
"var": {
|
||||
"index": [1, 99, [200, 300]]
|
||||
},
|
||||
"obs": {
|
||||
"annotation_value": [
|
||||
{"name": "louvain", "values": ["NK cells", "CD8 T cells"]},
|
||||
{"name": "n_counts", "min": 3000},
|
||||
],
|
||||
"index": [1, 99, [1000, 2000]],
|
||||
},
|
||||
"index": [1, 99, [1000, 2000]]
|
||||
}
|
||||
}
|
||||
}
|
||||
data = self.data.filter_dataframe(filter_["filter"])
|
||||
@@ -89,14 +116,13 @@ class UtilTest(unittest.TestCase):
|
||||
self.assertEqual(self.data.schema, schema)
|
||||
|
||||
def test_schema_produces_error(self):
|
||||
self.data.data.obs["time"] = Series(
|
||||
list([time.time() for i in range(self.data.cell_count)]), dtype="datetime64[ns]"
|
||||
)
|
||||
self.data.data.obs["time"] = Series(list([time.time() for i in range(self.data.cell_count)]),
|
||||
dtype="datetime64[ns]")
|
||||
with pytest.raises(TypeError):
|
||||
self.data._create_schema()
|
||||
|
||||
def test_config(self):
|
||||
self.assertEqual(self.data.features["layout"]["obs"], {"available": True, "interactiveLimit": 50000})
|
||||
self.assertEqual(self.data.features["layout"]["obs"], {'available': True, 'interactiveLimit': 50000})
|
||||
|
||||
def test_layout(self):
|
||||
layout = self.data.layout(None)
|
||||
@@ -126,8 +152,16 @@ class UtilTest(unittest.TestCase):
|
||||
def test_filtered_annotation(self):
|
||||
filter_ = {
|
||||
"filter": {
|
||||
"obs": {"annotation_value": [{"name": "n_counts", "min": 3000}]},
|
||||
"var": {"annotation_value": [{"name": "name", "values": ["ATAD3C", "RER1"]}]},
|
||||
"obs": {
|
||||
"annotation_value": [
|
||||
{"name": "n_counts", "min": 3000},
|
||||
]
|
||||
},
|
||||
"var": {
|
||||
"annotation_value": [
|
||||
{"name": "name", "values": ["ATAD3C", "RER1"]},
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
annotations = self.data.annotation(filter_["filter"], "obs")
|
||||
@@ -138,16 +172,38 @@ class UtilTest(unittest.TestCase):
|
||||
self.assertEqual(len(annotations["data"]), 2)
|
||||
|
||||
def test_filtered_layout(self):
|
||||
filter_ = {"filter": {"obs": {"annotation_value": [{"name": "n_counts", "min": 3000}]}}}
|
||||
filter_ = {
|
||||
"filter": {
|
||||
"obs": {
|
||||
"annotation_value": [
|
||||
{"name": "n_counts", "min": 3000},
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
layout = self.data.layout(filter_["filter"])
|
||||
self.assertEqual(len(layout["coordinates"]), 497)
|
||||
|
||||
def test_diffexp_topN(self):
|
||||
f1 = {"filter": {"obs": {"index": [[0, 500]]}}}
|
||||
f2 = {"filter": {"obs": {"index": [[500, 1000]]}}}
|
||||
result = self.data.diffexp_topN(f1["filter"], f2["filter"])
|
||||
def test_diffexp(self):
|
||||
f1 = {
|
||||
"filter": {
|
||||
"obs": {
|
||||
"index": [[0, 500]]
|
||||
}
|
||||
}
|
||||
}
|
||||
f2 = {
|
||||
"filter": {
|
||||
"obs": {
|
||||
"index": [[500, 1000]]
|
||||
}
|
||||
}
|
||||
}
|
||||
result = self.data.diffexp(f1["filter"], f2["filter"])
|
||||
self.assertEqual(len(result), 10)
|
||||
result = self.data.diffexp_topN(f1["filter"], f2["filter"], 20)
|
||||
var_idx = [i[0] for i in result]
|
||||
self.assertEqual(var_idx, sorted(var_idx))
|
||||
result = self.data.diffexp(f1["filter"], f2["filter"], 20)
|
||||
self.assertEqual(len(result), 20)
|
||||
|
||||
def test_data_frame(self):
|
||||
@@ -159,7 +215,15 @@ class UtilTest(unittest.TestCase):
|
||||
self.assertEqual(len(data_frame_var["obs"]), 2638)
|
||||
|
||||
def test_filtered_data_frame(self):
|
||||
filter_ = {"filter": {"obs": {"annotation_value": [{"name": "n_counts", "min": 3000}]}}}
|
||||
filter_ = {
|
||||
"filter": {
|
||||
"obs": {
|
||||
"annotation_value": [
|
||||
{"name": "n_counts", "min": 3000},
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
data_frame_obs = self.data.data_frame(filter_["filter"], "obs")
|
||||
self.assertEqual(len(data_frame_obs["var"]), 1838)
|
||||
self.assertEqual(len(data_frame_obs["obs"]), 497)
|
||||
@@ -173,7 +237,15 @@ class UtilTest(unittest.TestCase):
|
||||
|
||||
def test_data_single_gene(self):
|
||||
for axis in ["obs", "var"]:
|
||||
filter_ = {"filter": {"var": {"annotation_value": [{"name": "name", "values": ["RER1"]}]}}}
|
||||
filter_ = {
|
||||
"filter": {
|
||||
"var": {
|
||||
"annotation_value": [
|
||||
{"name": "name", "values": ["RER1"]},
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
data_frame_var = self.data.data_frame(filter_["filter"], axis)
|
||||
if axis == "obs":
|
||||
self.assertEqual(type(data_frame_var["var"][0]), int)
|
||||
@@ -182,5 +254,5 @@ class UtilTest(unittest.TestCase):
|
||||
self.assertEqual(type(data_frame_var["obs"][0]), int)
|
||||
self.assertIsInstance(data_frame_var["var"][0], (list, tuple))
|
||||
|
||||
if __name__ == "__main__":
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
||||
@@ -1,23 +1,14 @@
|
||||
from setuptools import setup, find_packages
|
||||
import sys
|
||||
|
||||
if sys.version_info[0:2] != (3, 6):
|
||||
raise ImportError(
|
||||
"cellxgene currently only supports python 3.6. Python 3.7 is known to fail; we will look at supporting "
|
||||
"versions other than 3.6 in the future."
|
||||
"See https://github.com/chanzuckerberg/cellxgene#conda-and-virtual-environments "
|
||||
"for more help with installation."
|
||||
)
|
||||
|
||||
with open("README.md", "rb") as fh:
|
||||
long_description = fh.read().decode()
|
||||
with open("README.md", "r") as fh:
|
||||
long_description = fh.read()
|
||||
|
||||
with open("server/requirements.txt") as fh:
|
||||
requirements = fh.read().splitlines()
|
||||
|
||||
setup(
|
||||
name="cellxgene",
|
||||
version="0.4.0",
|
||||
version="0.0.2",
|
||||
packages=find_packages(),
|
||||
url="https://github.com/chanzuckerberg/cellxgene",
|
||||
license="MIT",
|
||||
@@ -25,24 +16,18 @@ setup(
|
||||
author_email="cweaver@chanzuckerberg.com",
|
||||
description="Web application for exploration of large scale scRNA-seq datasets",
|
||||
long_description=long_description,
|
||||
long_description_content_type="text/markdown",
|
||||
install_requires=requirements,
|
||||
include_package_data=True,
|
||||
zip_safe=False,
|
||||
classifiers=[
|
||||
"Framework :: Flask",
|
||||
"Intended Audience :: Science/Research",
|
||||
"License :: OSI Approved :: MIT License",
|
||||
"Natural Language :: English",
|
||||
"Operating System :: POSIX",
|
||||
"Operating System :: Unix",
|
||||
"Operating System :: MacOS :: MacOS X",
|
||||
"Programming Language :: JavaScript",
|
||||
classifiers=(
|
||||
"Programming Language :: Python :: 3",
|
||||
"Programming Language :: Python :: 3.6",
|
||||
"Programming Language :: Python :: 3 :: Only",
|
||||
"Topic :: Scientific/Engineering :: Bio-Informatics",
|
||||
],
|
||||
entry_points={"console_scripts": ["cellxgene = server.cli.cli:cli"]},
|
||||
extras_require=dict(louvain=["python-igraph", "louvain>=0.6"]),
|
||||
"License :: OSI Approved :: MIT License",
|
||||
),
|
||||
entry_points={
|
||||
"console_scripts":
|
||||
["cellxgene = server.cli.cli:cli"]
|
||||
},
|
||||
extras_require=dict(
|
||||
louvain=['python-igraph', 'louvain>=0.6'],
|
||||
),
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user