def deseq2_size_factors(counts, meta, design):
    """
    Get size factors for counts using DESeq2.

    Parameters
    ----------
    counts : pandas.DataFrame
        Counts to pass to DESeq2.

    meta : pandas.DataFrame
        Pandas dataframe whose index matches the columns of counts. This is
        passed to DESeq2's colData.

    design : str
        Design like ~subject_id that will be passed to DESeq2. The design
        variables should match columns in meta.

    Returns
    -------
    sf : pandas.Series
        Series whose index matches the columns of counts and whose values are
        the size factors from DESeq2. Divide each column by its size factor to
        obtain normalized counts.

    """
    import rpy2.robjects as r
    from rpy2.robjects import pandas2ri
    pandas2ri.activate()
    r.r('suppressMessages(library(DESeq2))')
    r.globalenv['counts'] = counts
    r.globalenv['meta'] = meta
    r.r('dds = DESeqDataSetFromMatrix(countData=counts, colData=meta, '
        'design={})'.format(design))
    r.r('dds = estimateSizeFactors(dds)')
    r.r('sf = sizeFactors(dds)')
    sf = r.globalenv['sf']
    return pd.Series(sf, index=counts.columns)
def deseq2_size_factors ( counts , meta , design ) : import rpy2 . robjects as r from rpy2 . robjects import pandas2ri pandas2ri . activate ( ) r . r ( 'suppressMessages(library(DESeq2))' ) r . globalenv [ 'counts' ] = counts r . globalenv [ 'meta' ] = meta r . r ( 'dds = DESeqDataSetFromMatrix(countData=counts, colData=meta, ' 'design={})' . format ( design ) ) r . r ( 'dds = estimateSizeFactors(dds)' ) r . r ( 'sf = sizeFactors(dds)' ) sf = r . globalenv [ 'sf' ] return pd . Series ( sf , index = counts . columns )
Get size factors for counts using DESeq2.

    Parameters
    ----------
    counts : pandas.DataFrame
        Counts to pass to DESeq2.

    meta : pandas.DataFrame
        Pandas dataframe whose index matches the columns of counts. This is
        passed to DESeq2's colData.

    design : str
        Design like ~subject_id that will be passed to DESeq2. The design
        variables should match columns in meta.

    Returns
    -------
    sf : pandas.Series
        Series whose index matches the columns of counts and whose values are
        the size factors from DESeq2. Divide each column by its size factor to
        obtain normalized counts.