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Merge pull request #88 from AlexsLemonade/jashapiro/87-anndata-updates
Port Anndata merge changes from scpca-nf
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modules/merge-sce/resources/usr/bin/move_counts_anndata.py
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modules/merge-sce/resources/usr/bin/reformat_anndata.py
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| 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 converted 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]) | ||
|
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||
|
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| # 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) |
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