gseapy/algorithm.py
def gsea_pval(es, esnull):
    """Compute nominal p-value.

    From article (PNAS):
    estimate nominal p-value for S from esnull by using the positive
    or negative portion of the distribution corresponding to the sign
    of the observed ES(S).
    """

    # to speed up, using numpy function to compute pval in parallel.
    condlist = [ es < 0, es >=0]
    choicelist = [np.sum(esnull < es.reshape(len(es),1), axis=1)/ np.sum(esnull < 0, axis=1),
                  np.sum(esnull >= es.reshape(len(es),1), axis=1)/ np.sum(esnull >= 0, axis=1)]
    pval = np.select(condlist, choicelist)

    return pval
def gsea_pval(es, esnull):
    # to speed up, using numpy function to compute pval in parallel.
    condlist = [ es < 0, es >=0]
    choicelist = [np.sum(esnull < es.reshape(len(es),1), axis=1)/ np.sum(esnull < 0, axis=1),
                  np.sum(esnull >= es.reshape(len(es),1), axis=1)/ np.sum(esnull >= 0, axis=1)]
    pval = np.select(condlist, choicelist)

    return pval
def gsea_pval ( es , esnull ) : # to speed up, using numpy function to compute pval in parallel. condlist = [ es < 0 , es >= 0 ] choicelist = [ np . sum ( esnull < es . reshape ( len ( es ) , 1 ) , axis = 1 ) / np . sum ( esnull < 0 , axis = 1 ) , np . sum ( esnull >= es . reshape ( len ( es ) , 1 ) , axis = 1 ) / np . sum ( esnull >= 0 , axis = 1 ) ] pval = np . select ( condlist , choicelist ) return pval
Compute nominal p-value.

    From article (PNAS):
    estimate nominal p-value for S from esnull by using the positive
    or negative portion of the distribution corresponding to the sign
    of the observed ES(S).
Compute nominal p - value .