diff decoupler_pseudobulk.py @ 0:1e8697931d73 draft

planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit c8c39f14eeee6e7a6d097fd0cb9430b12793eb8b

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/decoupler_pseudobulk.py	Thu Nov 09 11:36:08 2023 +0000
@@ -0,0 +1,367 @@
+import argparse
+
+import anndata
+import decoupler
+import pandas as pd
+
+
+def get_pseudobulk(
+    adata,
+    sample_col,
+    groups_col,
+    layer=None,
+    mode="sum",
+    min_cells=10,
+    min_counts=1000,
+    use_raw=False,
+):
+    """
+    >>> import scanpy as sc
+    >>> adata = sc.datasets.pbmc68k_reduced()
+    >>> adata.X = abs(adata.X).astype(int)
+    >>> pseudobulk = get_pseudobulk(adata, "bulk_labels", "louvain")
+    """
+
+    return decoupler.get_pseudobulk(
+        adata,
+        sample_col=sample_col,
+        groups_col=groups_col,
+        layer=layer,
+        mode=mode,
+        use_raw=use_raw,
+        min_cells=min_cells,
+        min_counts=min_counts,
+    )
+
+
+def prepend_c_to_index(index_value):
+    if index_value and index_value[0].isdigit():
+        return "C" + index_value
+    return index_value
+
+
+# write results for loading into DESeq2
+def write_DESeq2_inputs(pdata, layer=None, output_dir="", factor_fields=None):
+    """
+    >>> import scanpy as sc
+    >>> adata = sc.datasets.pbmc68k_reduced()
+    >>> adata.X = abs(adata.X).astype(int)
+    >>> pseudobulk = get_pseudobulk(adata, "bulk_labels", "louvain")
+    >>> write_DESeq2_inputs(pseudobulk)
+    """
+    # add / to output_dir if is not empty or if it doesn't end with /
+    if output_dir != "" and not output_dir.endswith("/"):
+        output_dir = output_dir + "/"
+    obs_for_deseq = pdata.obs.copy()
+    # replace any index starting with digits to start with C instead.
+    obs_for_deseq.rename(index=prepend_c_to_index, inplace=True)
+    # avoid dash that is read as point on R colnames.
+    obs_for_deseq.index = obs_for_deseq.index.str.replace("-", "_")
+    obs_for_deseq.index = obs_for_deseq.index.str.replace(" ", "_")
+    col_metadata_file = f"{output_dir}col_metadata.tsv"
+    # write obs to a col_metadata file
+    if factor_fields:
+        # only output the index plus the columns in factor_fields in that order
+        obs_for_deseq[factor_fields].to_csv(col_metadata_file, sep="\t", index=True)
+    else:
+        obs_for_deseq.to_csv(col_metadata_file, sep="\t", index=True)
+    # write var to a gene_metadata file
+    pdata.var.to_csv(f"{output_dir}gene_metadata.tsv", sep="\t", index=True)
+    # write the counts matrix of a specified layer to file
+    if layer is None:
+        # write the X numpy matrix transposed to file
+        df = pd.DataFrame(pdata.X.T, index=pdata.var.index, columns=obs_for_deseq.index)
+    else:
+        df = pd.DataFrame(
+            pdata.layers[layer].T, index=pdata.var.index, columns=obs_for_deseq.index
+        )
+    df.to_csv(f"{output_dir}counts_matrix.tsv", sep="\t", index_label="")
+
+
+def plot_pseudobulk_samples(
+    pseudobulk_data,
+    groupby,
+    figsize=(10, 10),
+    save_path=None,
+):
+    """
+    >>> import scanpy as sc
+    >>> adata = sc.datasets.pbmc68k_reduced()
+    >>> adata.X = abs(adata.X).astype(int)
+    >>> pseudobulk = get_pseudobulk(adata, "bulk_labels", "louvain")
+    >>> plot_pseudobulk_samples(pseudobulk, groupby=["bulk_labels", "louvain"], figsize=(10, 10))
+    """
+    fig = decoupler.plot_psbulk_samples(
+        pseudobulk_data, groupby=groupby, figsize=figsize, return_fig=True
+    )
+    if save_path:
+        fig.savefig(f"{save_path}/pseudobulk_samples.png")
+    else:
+        fig.show()
+
+
+def plot_filter_by_expr(
+    pseudobulk_data, group, min_count=None, min_total_count=None, save_path=None
+):
+    """
+    >>> import scanpy as sc
+    >>> adata = sc.datasets.pbmc68k_reduced()
+    >>> adata.X = abs(adata.X).astype(int)
+    >>> pseudobulk = get_pseudobulk(adata, "bulk_labels", "louvain")
+    >>> plot_filter_by_expr(pseudobulk, group="bulk_labels", min_count=10, min_total_count=200)
+    """
+    fig = decoupler.plot_filter_by_expr(
+        pseudobulk_data,
+        group=group,
+        min_count=min_count,
+        min_total_count=min_total_count,
+        return_fig=True,
+    )
+    if save_path:
+        fig.savefig(f"{save_path}/filter_by_expr.png")
+    else:
+        fig.show()
+
+
+def filter_by_expr(pdata, min_count=None, min_total_count=None):
+    """
+    >>> import scanpy as sc
+    >>> adata = sc.datasets.pbmc68k_reduced()
+    >>> adata.X = abs(adata.X).astype(int)
+    >>> pseudobulk = get_pseudobulk(adata, "bulk_labels", "louvain")
+    >>> pdata_filt = filter_by_expr(pseudobulk, min_count=10, min_total_count=200)
+    """
+    genes = decoupler.filter_by_expr(
+        pdata, min_count=min_count, min_total_count=min_total_count
+    )
+    return pdata[:, genes].copy()
+
+
+def check_fields(fields, adata, obs=True, context=None):
+    """
+    >>> import scanpy as sc
+    >>> adata = sc.datasets.pbmc68k_reduced()
+    >>> check_fields(["bulk_labels", "louvain"], adata, obs=True)
+    """
+
+    legend = ""
+    if context:
+        legend = f", passed in {context},"
+    if obs:
+        if not set(fields).issubset(set(adata.obs.columns)):
+            raise ValueError(
+                f"Some of the following fields {legend} are not present in adata.obs: {fields}. Possible fields are: {list(set(adata.obs.columns))}"
+            )
+    else:
+        if not set(fields).issubset(set(adata.var.columns)):
+            raise ValueError(
+                f"Some of the following fields {legend} are not present in adata.var: {fields}. Possible fields are: {list(set(adata.var.columns))}"
+            )
+
+
+def main(args):
+    # Load AnnData object from file
+    adata = anndata.read_h5ad(args.adata_file)
+
+    # Merge adata.obs fields specified in args.adata_obs_fields_to_merge
+    if args.adata_obs_fields_to_merge:
+        # first split potential groups by ":" and iterate over them
+        for group in args.adata_obs_fields_to_merge.split(":"):
+            fields = group.split(",")
+            check_fields(fields, adata)
+            adata = merge_adata_obs_fields(fields, adata)
+
+    check_fields([args.groupby, args.sample_key], adata)
+
+    factor_fields = None
+    if args.factor_fields:
+        factor_fields = args.factor_fields.split(",")
+        check_fields(factor_fields, adata)
+
+    print(f"Using mode: {args.mode}")
+    # Perform pseudobulk analysis
+    pseudobulk_data = get_pseudobulk(
+        adata,
+        sample_col=args.sample_key,
+        groups_col=args.groupby,
+        layer=args.layer,
+        mode=args.mode,
+        use_raw=args.use_raw,
+        min_cells=args.min_cells,
+        min_counts=args.min_counts,
+    )
+
+    # Plot pseudobulk samples
+    plot_pseudobulk_samples(
+        pseudobulk_data,
+        args.groupby,
+        save_path=args.save_path,
+        figsize=args.plot_samples_figsize,
+    )
+
+    plot_filter_by_expr(
+        pseudobulk_data,
+        group=args.groupby,
+        min_count=args.min_counts,
+        min_total_count=args.min_total_counts,
+        save_path=args.save_path,
+    )
+
+    # Filter by expression if enabled
+    if args.filter_expr:
+        filtered_adata = filter_by_expr(
+            pseudobulk_data,
+            min_count=args.min_counts,
+            min_total_count=args.min_total_counts,
+        )
+
+        pseudobulk_data = filtered_adata
+
+    # Save the pseudobulk data
+    if args.anndata_output_path:
+        pseudobulk_data.write_h5ad(args.anndata_output_path, compression="gzip")
+
+    write_DESeq2_inputs(
+        pseudobulk_data, output_dir=args.deseq2_output_path, factor_fields=factor_fields
+    )
+
+
+def merge_adata_obs_fields(obs_fields_to_merge, adata):
+    """
+    Merge adata.obs fields specified in args.adata_obs_fields_to_merge
+
+    Parameters
+    ----------
+    obs_fields_to_merge : str
+        Fields in adata.obs to merge, comma separated
+    adata : anndata.AnnData
+        The AnnData object
+
+    Returns
+    -------
+    anndata.AnnData
+        The merged AnnData object
+
+    docstring tests:
+    >>> import scanpy as sc
+    >>> ad = sc.datasets.pbmc68k_reduced()
+    >>> ad = merge_adata_obs_fields(["bulk_labels","louvain"], ad)
+    >>> ad.obs.columns
+    Index(['bulk_labels', 'n_genes', 'percent_mito', 'n_counts', 'S_score',
+           'G2M_score', 'phase', 'louvain', 'bulk_labels_louvain'],
+          dtype='object')
+    """
+    field_name = "_".join(obs_fields_to_merge)
+    for field in obs_fields_to_merge:
+        if field not in adata.obs.columns:
+            raise ValueError(f"The '{field}' column is not present in adata.obs.")
+        if field_name not in adata.obs.columns:
+            adata.obs[field_name] = adata.obs[field].astype(str)
+        else:
+            adata.obs[field_name] = (
+                adata.obs[field_name] + "_" + adata.obs[field].astype(str)
+            )
+    return adata
+
+
+if __name__ == "__main__":
+    # Create argument parser
+    parser = argparse.ArgumentParser(
+        description="Perform pseudobulk analysis on an AnnData object"
+    )
+
+    # Add arguments
+    parser.add_argument("adata_file", type=str, help="Path to the AnnData file")
+    parser.add_argument(
+        "-m",
+        "--adata_obs_fields_to_merge",
+        type=str,
+        help="Fields in adata.obs to merge, comma separated. You can have more than one set of fields, separated by semi-colon ;",
+    )
+    parser.add_argument(
+        "--groupby",
+        type=str,
+        required=True,
+        help="The column in adata.obs that defines the groups",
+    )
+    parser.add_argument(
+        "--sample_key",
+        required=True,
+        type=str,
+        help="The column in adata.obs that defines the samples",
+    )
+    # add argument for layer
+    parser.add_argument(
+        "--layer",
+        type=str,
+        default=None,
+        help="The name of the layer of the AnnData object to use",
+    )
+    # add argument for mode
+    parser.add_argument(
+        "--mode",
+        type=str,
+        default="sum",
+        help="The mode for Decoupler pseudobulk analysis",
+        choices=["sum", "mean", "median"],
+    )
+    # add boolean argument for use_raw
+    parser.add_argument(
+        "--use_raw",
+        action="store_true",
+        default=False,
+        help="Whether to use the raw part of the AnnData object",
+    )
+    # add argument for min_cells
+    parser.add_argument(
+        "--min_cells",
+        type=int,
+        default=10,
+        help="Minimum number of cells for pseudobulk analysis",
+    )
+    parser.add_argument(
+        "--save_path", type=str, help="Path to save the plot (optional)"
+    )
+    parser.add_argument(
+        "--min_counts",
+        type=int,
+        help="Minimum count threshold for filtering by expression",
+    )
+    parser.add_argument(
+        "--min_total_counts",
+        type=int,
+        help="Minimum total count threshold for filtering by expression",
+    )
+    parser.add_argument(
+        "--anndata_output_path",
+        type=str,
+        help="Path to save the filtered AnnData object or pseudobulk data",
+    )
+    parser.add_argument(
+        "--filter_expr", action="store_true", help="Enable filtering by expression"
+    )
+    parser.add_argument(
+        "--factor_fields",
+        type=str,
+        help="Comma separated list of fields for the factors",
+    )
+    parser.add_argument(
+        "--deseq2_output_path",
+        type=str,
+        help="Path to save the DESeq2 inputs",
+        required=True,
+    )
+    parser.add_argument(
+        "--plot_samples_figsize",
+        type=int,
+        default=[10, 10],
+        nargs=2,
+        help="Size of the samples plot as a tuple (two arguments)",
+    )
+    parser.add_argument("--plot_filtering_figsize", type=int, default=[10, 10], nargs=2)
+
+    # Parse the command line arguments
+    args = parser.parse_args()
+
+    # Call the main function
+    main(args)