add_metrics

Add Calling Cards metrics to the given DataFrame.

The kwargs parameter is used to pass additional arguments into underlying functions. Currently, the following are configured: - pseudocount: pseudocount to use when calculating both the enrichment and poisson pvalue. See either function for more documentation. Passed through kwargs so that if none is passed to add_metrics, the default in enrichment_vectorized() and poisson_pval_vectorized() is used.

:param dataframe: a pandas DataFrame of promoter regions. :type dataframe: DataFrame

:return: a pandas DataFrame of promoter regions with Calling Cards metrics. :rtype: DataFrame

Source code in callingcardstools/PeakCalling/yeast/call_peaks.py

def add_metrics(dataframe: pd.DataFrame, **kwargs) -> pd.DataFrame:
    """
    Add Calling Cards metrics to the given DataFrame.

    The kwargs parameter is used to pass additional arguments into underlying
    functions. Currently, the following are configured:
    - pseudocount: pseudocount to use when calculating both the enrichment and
        poisson pvalue. See either function for more documentation. Passed through
        kwargs so that if none is passed to add_metrics, the default in
        enrichment_vectorized() and poisson_pval_vectorized() is used.

    :param dataframe: a pandas DataFrame of promoter regions.
    :type dataframe: DataFrame

    :return: a pandas DataFrame of promoter regions with Calling Cards
        metrics.
    :rtype: DataFrame
    """
    dataframe["callingcards_enrichment"] = enrichment_vectorized(
        dataframe["background_total_hops"],
        dataframe["experiment_total_hops"],
        dataframe["background_hops"],
        dataframe["experiment_hops"],
        **kwargs,
    )

    dataframe["poisson_pval"] = poisson_pval_vectorized(
        dataframe["background_total_hops"],
        dataframe["experiment_total_hops"],
        dataframe["background_hops"],
        dataframe["experiment_hops"],
        **kwargs,
    )

    dataframe["hypergeometric_pval"] = hypergeom_pval_vectorized(
        dataframe["background_total_hops"],
        dataframe["experiment_total_hops"],
        dataframe["background_hops"],
        dataframe["experiment_hops"],
    )

    return dataframe