greatpy.tl.set_threshold

greatpy.tl.set_threshold(enrichment_df, colname, alpha=0.05)

Delete rows according to the p-value of the column taken as argument. By default the alpha value is 0.05

Parameters:

enrichment_df : pd.DataFrame

Dataframe with the enrichment computed by enrichment function

colname : str

name of the column to take the p-value from

alpha : float

alpha value used for the threshold filter

Default is 0.05

Returns:

dataframe with the rows deleted according to the p-value threshold

Return type:

pd.DataFrame

Examples

>>> test,regdom,size,ann = loader(
    "../data/tests/test_data/input/03_srf_hg19.bed",
    "../data/human/hg19/regulatory_domain.bed",
    "../data/human/hg19/chr_size.bed",
    "../data/human/ontologies.csv"
    )
>>> enrichment = great.tl.enrichment(
    "../data/tests/test_data/input/03_srf_hg19.bed",
    "../data/human/hg19/regulatory_domain.bed",
    "../data/human/hg19/chr_size.bed",
    "../data/human/ontologies.csv",
    binom=True,
    hypergeom=True
    )
>>> enrichment.shape[0]
...    594

>>> significant = set_threshold(enrichment,colname="binom_p_value",alpha=0.05)
>>> significant.shape[0]
...    310