Metadata-Version: 2.1
Name: scanpy-scripts
Version: 0.2.5
Summary: Scripts for using scanpy from the command line
Home-page: https://github.com/ebi-gene-expression-group/scanpy-scripts
Author: nh3
Author-email: nh3@users.noreply.github.com
License: UNKNOWN
Platform: UNKNOWN
Description-Content-Type: text/markdown
Requires-Dist: packaging
Requires-Dist: anndata (<0.6.20)
Requires-Dist: scipy (<1.3.0,>=1.2.0)
Requires-Dist: matplotlib
Requires-Dist: pandas
Requires-Dist: h5py (<2.10)
Requires-Dist: scanpy (<1.4.4,>=1.4.2)
Requires-Dist: louvain
Requires-Dist: leidenalg
Requires-Dist: loompy (<3.0.0,>=2.0.0)
Requires-Dist: MulticoreTSNE
Requires-Dist: Click

# scanpy-scripts
Scripts for using scanpy from the command line

In order to wrap scanpy's internal workflow in any given workflow language, it's important to have scripts to call each of those steps. These scripts are being written here, and will improve in completeness as time progresses. 

## Install

```bash
conda install scanpy-scripts
# or
pip3 install scanpy-scripts
```

## Test installation

There is an example script included:

```bash
scanpy-scripts-tests.sh
```

This downloads [a well-known test 10X dataset]('https://s3-us-west-2.amazonaws.com/10x.files/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz) and executes all of the scripts described below.

## Commands

Available commands are described below. Each has usage instructions available via --help, consult function documentation in scanpy for further details.

```
Usage: scanpy-cli [OPTIONS] COMMAND [ARGS]...

  Command line interface to [scanpy](https://github.com/theislab/scanpy)

Options:
  --debug              Print debug information
  --verbosity INTEGER  Set scanpy verbosity
  --version            Show the version and exit.
  --help               Show this message and exit.

Commands:
  read      Read 10x data and save in specified format.
  filter    Filter data based on specified conditions.
  norm      Normalise data per cell.
  hvg       Find highly variable genes.
  scale     Scale data per gene.
  regress   Regress-out observation variables.
  pca       Dimensionality reduction by PCA.
  neighbor  Compute a neighbourhood graph of observations.
  embed     Embed cells into two-dimensional space.
  cluster   Cluster cells into sub-populations.
  diffexp   Find markers for each clusters.
  paga      Trajectory inference by abstract graph analysis.
  dpt       Calculate diffusion pseudotime relative to the root cells.
  plot      Visualise data.
  ```


