-
Notifications
You must be signed in to change notification settings - Fork 2
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #779 from AlexsLemonade/development
Sync changes from `development` into `main` for v0.8.3
- Loading branch information
Showing
9 changed files
with
180 additions
and
75 deletions.
There are no files selected for viewing
This file was deleted.
Oops, something went wrong.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,118 @@ | ||
| #!/usr/bin/env python3 | ||
| """ | ||
| This script takes an AnnData object and checks for the `logcounts` in layers. | ||
| If present, `logcounts` is moved to `X` and `X` (which has the raw counts) is moved to `raw.X` | ||
| In addition, any DataFrames in `obsm` are conerted to ndarrays, highly variable genes are converted to a `var` column. | ||
| If a `pca_meta_file` is provided, PCA variance statistics and standard creation values are in the format expected by scanpy. | ||
| """ | ||
|
|
||
| import argparse | ||
| import os | ||
| import re | ||
|
|
||
| import anndata | ||
| import pandas as pd | ||
|
|
||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument( | ||
| "-i", | ||
| "--anndata_file", | ||
| dest="anndata_file", | ||
| required=True, | ||
| help="Path to HDF5 file with processed AnnData object", | ||
| ) | ||
| parser.add_argument( | ||
| "--pca_meta_file", | ||
| dest="pca_meta_file", | ||
| required=False, | ||
| help="Path to file with a table of variance explained by each PCA component", | ||
| ) | ||
| parser.add_argument( | ||
| "--pca_not_centered", | ||
| dest="pca_centered", | ||
| action="store_false", | ||
| help="Indicate that the PCA table is not zero-centered", | ||
| ) | ||
| parser.add_argument( | ||
| "--hvg_name", | ||
| dest="hvg_name", | ||
| default="highly_variable_genes", | ||
| help=( | ||
| "Indicate the name used to store highly variable genes associated with the PCA in the exported AnnData object." | ||
| "Use the value 'none' if no highly variable genes were used." | ||
| ), | ||
| ) | ||
| parser.add_argument( | ||
| "-u", | ||
| "--uncompressed", | ||
| dest="compress", | ||
| action="store_false", | ||
| help="Output an uncompressed HDF5 file", | ||
| ) | ||
|
|
||
| args = parser.parse_args() | ||
|
|
||
| # compile extension regex | ||
| file_ext = re.compile(r"\.(hdf5|h5|h5ad)$", re.IGNORECASE) | ||
|
|
||
| # check that input file exists, if it does exist, make sure it's an h5 file | ||
| if not os.path.exists(args.anndata_file): | ||
| raise FileExistsError("`input_anndata` does not exist.") | ||
| elif not file_ext.search(args.anndata_file): | ||
| raise ValueError( | ||
| "input_anndata must end in either .hdf5, .h5, or .h5ad, and contain a processed AnnData adata." | ||
| ) | ||
|
|
||
| # if there is a pca_meta_file, check that it exists and has a tsv extension | ||
| if args.pca_meta_file: | ||
| if not os.path.exists(args.pca_meta_file): | ||
| raise FileExistsError("`pca_meta_file` does not exist.") | ||
| elif not args.pca_meta_file.casefold().endswith(".tsv"): | ||
| raise ValueError("`pca_meta_file` must end in .tsv.") | ||
|
|
||
| # read in anndata | ||
| adata = anndata.read_h5ad(args.anndata_file) | ||
|
|
||
| # if logcounts is present | ||
| if "logcounts" in adata.layers: | ||
| # move X to raw.X by creating the raw adata | ||
| adata.raw = adata | ||
| # move logcounts to X and rename | ||
| adata.X = adata.layers["logcounts"] | ||
| adata.uns["X_name"] = "logcounts" | ||
| del adata.layers["logcounts"] | ||
|
|
||
| # convert DataFrames in obsm to ndarrays | ||
| for key, value in adata.obsm.items(): | ||
| if isinstance(value, pd.DataFrame): | ||
| adata.obsm[key] = value.to_numpy() | ||
|
|
||
| # convert highly variable genes to a column if given | ||
| use_hvg = args.hvg_name.casefold() != "none" | ||
| if use_hvg: | ||
| if args.hvg_name not in adata.uns.keys(): | ||
| raise ValueError("`hvg_name` must be present in the `uns` data for the object") | ||
| adata.var["highly_variable"] = adata.var.gene_ids.isin(adata.uns[args.hvg_name]) | ||
|
|
||
|
|
||
| # add pca adata to uns if pca_meta_file is provided in the format created by scanpy | ||
| if args.pca_meta_file: | ||
| pca_meta = pd.read_csv(args.pca_meta_file, sep="\t", index_col=0) | ||
| if "variance" not in pca_meta.columns or "variance_ratio" not in pca_meta.columns: | ||
| raise ValueError( | ||
| "`pca_meta_file` must contain columns `variance` and `variance_ratio`" | ||
| ) | ||
| pca_object = { | ||
| "param": { | ||
| "zero_center": args.pca_centered, | ||
| "use_highly_variable": use_hvg, | ||
| "mask_var": ("highly_variable" if use_hvg else None), | ||
| }, | ||
| "variance": pca_meta["variance"].to_numpy(), | ||
| "variance_ratio": pca_meta["variance_ratio"].to_numpy(), | ||
| } | ||
| adata.uns["pca"] = pca_object | ||
|
|
||
| # export adata | ||
| adata.write_h5ad(args.anndata_file, compression="gzip" if args.compress else None) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.