comparison decoupler_pseudobulk.py @ 11:db14ac3f6b43 draft default tip

planemo upload for repository https://github.com/ebi-gene-expression-group/container-galaxy-sc-tertiary/ commit 487508282bda9dbb68138d5c7091f46ef54fe52a

10:97c2c52a7ab4

import argparse

import anndata
import decoupler
import pandas as pd


def get_pseudobulk(
    adata,

        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

    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,

        "--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(

11:db14ac3f6b43

import argparse

import anndata
import decoupler
import numpy as np
import pandas as pd


def get_pseudobulk(
    adata,

        mode=mode,
        use_raw=use_raw,
        min_cells=min_cells,
        min_counts=min_counts,
    )


def create_pseudo_replicates(adata, sample_key, num_replicates, seed=None):
    """
    Create pseudo replicates for each sample in the sample_key groups.

    Parameters
    ----------
    adata : anndata.AnnData
        The AnnData object.
    sample_key : str
        The column in adata.obs that defines the samples.
    num_replicates : int
        Number of pseudo replicates to create per sample.

    Returns
    -------
    anndata.AnnData
        The AnnData object with pseudo replicates.

    Examples
    --------
    >>> import anndata
    >>> import pandas as pd
    >>> import numpy as np
    >>> data = {
    ...     'obs': pd.DataFrame({'sample': ['A', 'A', 'B', 'B']}),
    ...     'X': np.array([[1, 0], [0, 1], [1, 1], [0, 0]])
    ... }
    >>> adata = anndata.AnnData(X=data['X'], obs=data['obs'])
    >>> adata = create_pseudo_replicates(adata, 'sample', 2)
    >>> adata.obs['sample_pseudo'].tolist()
    ['A_rep1', 'A_rep2', 'B_rep1', 'B_rep2']
    """
    if seed is not None:
        np.random.seed(seed)

    new_sample_key = f"{sample_key}_pseudo"
    adata.obs[new_sample_key] = adata.obs[sample_key].astype(str)

    for sample in adata.obs[sample_key].unique():
        sample_indices = adata.obs[
            adata.obs[sample_key] == sample].index.to_numpy()
        np.random.shuffle(sample_indices)  # Shuffle the indices to randomize
        replicate_size = int(len(sample_indices) / num_replicates)
        for i in range(num_replicates):
            start_idx = i * replicate_size
            end_idx = start_idx + replicate_size
            replicate_indices = sample_indices[start_idx:end_idx]
            adata.obs.loc[replicate_indices, new_sample_key] = (
                adata.obs.loc[replicate_indices, new_sample_key] + f"_rep{i+1}"
            )

    return adata


def prepend_c_to_index(index_value):
    if index_value and index_value[0].isdigit():
        return "C" + index_value

    factor_fields = None
    if args.factor_fields:
        factor_fields = args.factor_fields.split(",")
        check_fields(factor_fields, adata)

    # Create pseudo replicates if specified
    if args.num_pseudo_replicates:
        adata = create_pseudo_replicates(
            adata, args.sample_key, args.num_pseudo_replicates, seed=args.seed
        )
        args.sample_key = f"{args.sample_key}_pseudo"

    print(f"Using mode: {args.mode}")
    # Perform pseudobulk analysis
    pseudobulk_data = get_pseudobulk(
        adata,

        "--min_total_counts",
        type=int,
        help="Minimum total count threshold for filtering by expression",
    )
    parser.add_argument(
        "--num_pseudo_replicates",
        type=int,
        choices=range(3, 1000),
        help="Number of pseudo replicates to create per sample (at least 3)",
        required=False
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=None,
        help="Random seed for pseudo replicate sampling",
    )
    parser.add_argument(
        "--anndata_output_path",
        type=str,
        help="Path to save the filtered AnnData object or pseudobulk data",
    )
    parser.add_argument(