0f520f2fd4
* Document how to install cellxgene prepare after pr #887 merged * formatting * remove reference to cellxgene[louvain]
5.0 KiB
layout, title, description
| layout | title | description |
|---|---|---|
| default | data | Data |
Using cellxgene prepare
What is cellxgene prepare?
prepare offers an easy command line interface (CLI) to preliminarily wrangle your data into the required format for previewing it with cellxgene.
What is cellxgene prepare not?
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.
While we'd like to support quick conversion from seurat and bioconductor, these packages don't currently output a python-parseable intermediate file type. In the meantime, you might check out the converters that are under early development.
What can cellxgene prepare do?
prepare uses scanpy to:
- Handle simple data normalization (from a recipe)
- Do basic preprocessing to run PCA and compute the neighbor graph
- Infer clusters
- Reduce dimensionality to generate embeddings.
You can control which steps to run and their methods (when applicable), via the CLI. The CLI also includes options for computing QC metrics, enforcing matrix sparcity, specifying index names, and plotting output.
To see a full list of available arguments and options, run cellxgene prepare --help.
How do I use cellxgene prepare?
As a quick example, let's construct a command to use prepare to take a raw expression matrix and generate a processed h5ad ready to visualize with cellxgene.
We'll start off using the raw data from the pbmc3k dataset. This dataset is described here, and is available as part of the scanpy API. For this example, we'll assume this raw data is stored in a file called pbmc3k-raw.h5ad.
Our prepare compose our command looks like this:
Let's look at what prepare is doing to our data, and how each step relates to the command above. You can see a walkthrough of what's going on under the hood for this example in this notebook.
1 - Compute quality control metrics and store this in our AnnData object for later inspection (A)
2 - Normalize the expression matrix using a basic preprocessing recipe (B)
3 - Do some preprocessing to run PCA and compute the neighbor graph (auto)
4 - Infer clusters with the Louvain algorithm and store these labels to visualize later (auto)
5 - Compute and store umap and tsne embeddings (C)
6 - Write results to file (D)
Example datasets to use with cellxgene
To download and use these datasets, run:
curl -O [URL]
unzip [filename.zip]
cellxgene launch [filename.h5ad] --open
Peripheral blood mononuclear cells
Healthy human PBMCs (10X).
- Source: 10X genomics
- Cells: 2,638
- File size: 19MB
- Raw data
- Processing
- Download:
curl -O https://cellxgene-example-data.czi.technology/pbmc3k.h5ad.zip
Tabula muris
20 organs and tissues from healthy mice (Smart-Seq2).
Rich metadata and annotations.
- Source: bioRxiv, CZBiohub
- Cells: 45,423
- File size: 174MB
- Raw data
- Processing
- Download:
curl -O https://cellxgene-example-data.czi.technology/tabula-muris.h5ad.zip
Tabula muris senis
22 organs and tissues from healthy mice at ages 3mo, 18mo, 21mo, and 24mo (Smart-Seq2).
Rich metadata and annotations.
- Source: bioRxiv, CZBiohub
- Cells: 81,478
- File size: 3.9GB
- Raw data [geo link coming soon!]
- Processing
- Download:
curl -O https://cellxgene-example-data.czi.technology/tabula-muris-senis.h5ad.zip