def get_patient_set(self, filters):
f1 = list(filters)
filter_df = pd.concat(f1, axis=1)
clinical_filter = filter_df.dropna().sum(1) == 0
keepers_o = H.true_index(clinical_filter)
keepers_o = keepers_o.intersection(self.mut_df.columns)
keepers_o = keepers_o.intersection(self.cna_df.columns)
return keepers_o
def get_patient_set(self, filters):
f1 = list(filters)
filter_df = pd.concat(f1, axis=1)
clinical_filter = filter_df.dropna().sum(1) == 0
keepers_o = H.true_index(clinical_filter)
keepers_o = keepers_o.intersection(self.mut_df.columns)
keepers_o = keepers_o.intersection(self.cna_df.columns)
keepers_o = keepers_o.intersection(self.surv.unstack().index)
keepers_o = keepers_o.intersection(self.rna_df.columns)
keepers_o = keepers_o.intersection(self.mirna_df.columns)
return keepers_o
def mut_filter(df, rate, binary_cutoff=12):
'''
Filter out mutation features, ensuring that a feature
is not entirely an artifact of mutation rate.
'''
df = df[df.sum(1) >= binary_cutoff]
cc = H.screen_feature(rate, rev_kruskal, df)
fc_apply = lambda s: fc(s, rate)
direction = df.apply(fc_apply, axis=1)
direction.name = 'direction'
cc = cc.join(direction)
cc = cc[cc.direction==False]
df = df.ix[H.true_index(cc.p > .01)]
df = df.dropna(axis=1)
return df
def mut_filter(df, rate, binary_cutoff=12):
"""
Filter out mutation features, ensuring that a feature
is not entirely an artifact of mutation rate.
"""
get_min_count = lambda s: s.value_counts().min() if len(s.unique()) > 1 else -1
df = df[df.apply(get_min_count, axis=1) > binary_cutoff]
cc = H.screen_feature(rate, rev_kruskal, df)
fc_apply = lambda s: fc(s, rate)
direction = df.apply(fc_apply, axis=1)
direction.name = 'direction'
cc = cc.join(direction)
#cc = cc[cc.direction == False]
#return cc
df = df.ix[H.true_index((cc.p > .01) | (cc.direction == True))]
df = df.dropna(axis=1)
return df
def mut_filter(df, rate, binary_cutoff=12):
"""
Filter out mutation features, ensuring that a feature
is not entirely an artifact of mutation rate.
"""
get_min_count = lambda s: s.value_counts().min() if len(s.unique()
) > 1 else -1
df = df[df.apply(get_min_count, axis=1) > binary_cutoff]
cc = H.screen_feature(rate, rev_kruskal, df)
fc_apply = lambda s: fc(s, rate)
direction = df.apply(fc_apply, axis=1)
direction.name = 'direction'
cc = cc.join(direction)
#cc = cc[cc.direction == False]
#return cc
df = df.ix[H.true_index((cc.p > .01) | (cc.direction == True))]
df = df.dropna(axis=1)
return df
