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Add calculate_qc_metrics to `prepare (#697)

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* Calculate QC metrics

* Add QC metrics to prepare section of readme

* Add pointer to scanpy qc metrics function

* Don't explicitly pass qc flag as arg

* Add explicit toggle for run-qc/skip-qc

* Move qc metrics calculation to separate step/function
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Sidney Bell
2019-04-09 08:56:07 -07:00
committed by GitHub
parent 7275d9d4dc
commit 34e5a91dc6
2 changed files with 16 additions and 2 deletions
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README.md
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@@ -70,7 +70,7 @@ 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://scanpy.readthedocs.io/en/latest/api/index.html#recipes).
This will load the input data, calculate QC metrics, 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://scanpy.readthedocs.io/en/latest/api/index.html#recipes).
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