fork/cellxgene
1
0
Fork 0
mirror of https://github.com/chanzuckerberg/cellxgene.git synced 2026-09-19 19:08:11 +08:00
Code Issues Packages Projects Releases Wiki Activity

[Merge on next release] Document how to install cellxgene prepare (#889)

Browse Source 
* Document how to install cellxgene prepare

after pr #887 merged

* formatting

* remove reference to cellxgene[louvain]
This commit is contained in:
Charlotte Weaver
2019-09-13 18:15:06 -07:00
committed by Bruce Martin
parent 84c815cc04
commit 0f520f2fd4
3 changed files with 26 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
docs/data.md
View File

@@ -14,6 +14,14 @@ description: Data
`cellxgene prepare` is not meant as a way to formally process or analyze your data. It's simply a utility for quickly wrangling your data into cellxgene-compatible format and computing a "vanilla" embedding so you can try out `cellxgene` and get a general sense of a dataset.
#### How do I install `cellxgene prepare`?
The `cellxgene prepare` command is an optional install that you can install alongside `cellxgene launch` by running
```
pip install cellxgene[prepare]
```
#### What input formats does it accept?
Currently, we accept `h5ad` and `loom` files, as well as `10x` directories, and are hoping to accept more formats in the future.
@@ -52,9 +60,9 @@ Let's look at what `prepare` is doing to our data, and how each step relates to
# Example datasets to use with cellxgene
**To download and use these datasets, run:**
`curl -O [URL]`
**To download and use these datasets, run:**
`curl -O [URL]`
`unzip [filename.zip]`
`cellxgene launch [filename.h5ad] --open`

12
docs/faq.md
View File

@@ -12,11 +12,13 @@ Currently, you can go straight into `cellxgene launch` with your own analyzed da
If your data is in a different format, and/or you still need to perform dimensionality reduction and clustering, `cellxgene` can do that for you with the `prepare` command. `cellxgene prepare` runs `scanpy` under the hood and can read in any format that is currently supported by `scanpy` (including mtx, loom, and more listed [here](https://scanpy.readthedocs.io/en/latest/api/index.html#reading)).
To add `cellxgene prepare` to your cellxgene installation run `pip install cellxgene[prepare]`.
The output of `cellxgene prepare` is a h5ad file with your computed clusters and tsne/umap projections that can be used in `cellxgene launch`.
#### I have a directory of 10X-Genomics data with _mtx_ files 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
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 `prepare` packages as described above. Just run
```
cellxgene prepare data/ --output=data-processed.h5ad --layout=umap
@@ -75,14 +77,6 @@ source ${ENV_NAME}/bin/activate
pip install cellxgene
```
#### 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, 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]
```
#### 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

23
docs/getting-started.md
View File

@@ -56,6 +56,18 @@ The `launch` command assumes that the data is stored in the `.h5ad` format from
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 install `cellxgene prepare` alongside `cellxgene launch`
```
pip install cellxgene[prepare]
```
If the aforementioned optional package installation fails, you can also install these packages directly:
```
pip install scanpy>=1.3.7 python-igraph louvain>=0.6
```
To prepare from an existing `.h5ad` file use
```
@@ -76,17 +88,6 @@ 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