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Very minor changes to elaborate in a few places

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Sidney Bell
2018-08-07 07:42:45 -07:00
parent 16d7fac88e
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@@ -21,84 +21,84 @@ Started in the context of the Human Cell Atlas Consortium, cellxgene hopes to bo
- OS: OSX, Windows, Linux
- python 3.6
- npm
- Google Chrome
- Google Chrome
**Clone project**
git clone https://github.com/chanzuckerberg/cellxgene.git
**Clone project**
git clone https://github.com/chanzuckerberg/cellxgene.git
**Install client**
**Install client**
cd cellxgene
./bin/build-client
./bin/build-client
**To use with virtual env for python**
(optional, but recommended)
ENV_NAME=cellxgene
python3 -m venv ${ENV_NAME}
source ${ENV_NAME}/bin/activate
**To use with virtual env for python**
(optional, but recommended)
**Install server**
python3 setup.py install
ENV_NAME=cellxgene
python3 -m venv ${ENV_NAME}
source ${ENV_NAME}/bin/activate
**Install server**
python3 setup.py install
**Run (with demo data)**
**Run (with demo data)**
cellxgene --title PBMC3K scanpy example-dataset/
*In google chrome, navigate to the viewer via the web address printed in your console.
*In google chrome, navigate to the viewer via the web address printed in your console.
E.g.,* `Running on http://0.0.0.0:5005/`
**Help**
cellxgene --help
_For help with the scanpy engine_
_For help with the scanpy engine_
cellxgene scanpy --help
## Using your own data
### Scanpy
To prepare you data you will need to format your data into AnnData format using scanpy and calculate PCA and nearest neighbors and save in h5ad format.
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.
1. [Load data into scanpy](https://scanpy.readthedocs.io/en/latest/api/index.html#exporting)
1. [Load data into scanpy](https://scanpy.readthedocs.io/en/latest/api/index.html#reading)
- Ensure that `obs`'s index is the cell names
- 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()`.
2. Calculate PCA
sc.pp.pca(data)
3. Calculate nearest neighbors (depending on layout algorithm)
sc.pp.pca(data) ## sc is scanpy.api
3. Calculate nearest neighbors (depending on layout algorithm)
```
# For umap 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
# 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")
```
5. Create config file (optional)
If you do not have a config file, the schema (metadata names, types, and categorical/continuous) will be inferred from the observations in the data file. Config file is required to be named 'data_schema.json' and located in the same directory as data file.
- The config file is a JSON format file with information on the metadata associated with the cells. The key is the column name in obs. The value is an object
- The config file is a JSON format file with information on the metadata associated with the cells. The key is the column name in obs. The value is an object
```
type: string, int, or float (what type the values are),
variabletype: categorical or continuous (categorical values are displayed as checkboxes, continuous values are displayed as a histogram)
displayname: (what the heading should be displayed as)
include: True/False (whether to display values on web interface)
variabletype: categorical or continuous (categorical values are displayed as checkboxes, continuous values are displayed as a histogram)
displayname: (what the heading should be displayed as)
include: True/False (whether to display values on web interface)
```
```
```
Example
{
"CellName": {