comparison COBRAxy/marea.py @ 313:e796d29076be draft

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312:a99667e35947

    Returns:
        None : mutates the comparisonResult dictionary in place with the p-values.
    """

    # pyDESeq2 is based on pandas, so we need to convert the data into a DataFrame and clean it from NaN values
    dataframe1 = pd.DataFrame(dataset1Data, index=ids)
    dataframe2 = pd.DataFrame(dataset2Data, index=ids)

    dataframe1_clean = dataframe1.dropna(axis=0, how="any").T.astype(int)
    dataframe2_clean = dataframe2.dropna(axis=0, how="any").T.astype(int)

    # pyDESeq2 works on a DataFrame with values and another with infos about samples and conditions
    dataframe = pd.concat([dataframe1_clean, dataframe2_clean], axis=0)
    metadata = pd.DataFrame(np.concatenate([np.full(dataframe1_clean.shape[0], "dataset1"), np.full(dataframe2_clean.shape[0], "dataset2")]), columns=["dataset"])
    metadata.index = dataframe.index

    # Prepare and run pyDESeq2
    inference = DefaultInference()
    dds = DeseqDataSet(counts=dataframe, metadata=metadata, design="~dataset", inference=inference, quiet=True, low_memory=True)
    dds.deseq2()

313:e796d29076be

    Returns:
        None : mutates the comparisonResult dictionary in place with the p-values.
    """

    # pyDESeq2 needs at least 2 replicates per sample so I check this
    if len(dataset1Data[0]) < 2 or len(dataset2Data[0]) < 2:
        raise ValueError("Datasets must have at least 2 replicates each")

    # pyDESeq2 is based on pandas, so we need to convert the data into a DataFrame and clean it from NaN values
    dataframe1 = pd.DataFrame(dataset1Data, index=ids)
    dataframe2 = pd.DataFrame(dataset2Data, index=ids)
    
    # pyDESeq2 requires datasets to be samples x reactions and integer values
    dataframe1_clean = dataframe1.dropna(axis=0, how="any").T.astype(int)
    dataframe2_clean = dataframe2.dropna(axis=0, how="any").T.astype(int)
    dataframe1_clean.index = [f"ds1_rep{i+1}" for i in range(dataframe1_clean.shape[0])]
    dataframe2_clean.index = [f"ds2_rep{j+1}" for j in range(dataframe2_clean.shape[0])]

    # pyDESeq2 works on a DataFrame with values and another with infos about how samples are split (like dataset class)
    dataframe = pd.concat([dataframe1_clean, dataframe2_clean], axis=0)
    metadata = pd.DataFrame({"dataset": (["dataset1"]*dataframe1_clean.shape[0] + ["dataset2"]*dataframe2_clean.shape[0])}, index=dataframe.index)

    # Ensure the index of the metadata matches the index of the dataframe
    if not dataframe.index.equals(metadata.index):
        raise ValueError("The index of the metadata DataFrame must match the index of the counts DataFrame.")

    # Prepare and run pyDESeq2
    inference = DefaultInference()
    dds = DeseqDataSet(counts=dataframe, metadata=metadata, design="~dataset", inference=inference, quiet=True, low_memory=True)
    dds.deseq2()