def get_feature_summary(pairwise_interactions, window_classes):
"""
Count the number of pairwise interactions between different window classes.
:param pairwise_interactions: Table of pairwise interactions.
:type pairwise_interactions: :class:`pandas.DataFrame`
:param window_classes: Table of windows and their classifications
:type window_classes: :class:`pandas.DataFrame`
:returns: A list of tuples of the form (first class, second class, count).
>>> pairwise_interactions = pd.DataFrame([('chr1', 10, 20, 0.75),
... ('chr2', 10, 20, 0.5),
... ('chr1', 10, 30, 0.4)],
... columns=['chrom', 'Pos_A', 'Pos_B',
... 'interaction'])
>>> pairwise_interactions
chrom Pos_A Pos_B interaction
0 chr1 10 20 0.75
1 chr2 10 20 0.50
2 chr1 10 30 0.40
>>> window_classification = pd.DataFrame([('chr1', 10, True, False),
... ('chr1', 20, False, True),
... ('chr2', 20, True, False),
... ('chr2', 30, False, True)],
... columns=['chrom', 'i', 'Enhancer', 'Gene'])
>>> window_classification
chrom i Enhancer Gene
0 chr1 10 True False
1 chr1 20 False True
2 chr2 20 True False
3 chr2 30 False True
>>> enrichment.get_feature_summary(pairwise_interactions, window_classification)
[('Enhancer', 'Enhancer', 0), ('Enhancer', 'Gene', 1), ('Gene', 'Gene', 0)]
"""
results = []
feature_classes = [col for col in window_classes.columns
if not col in ['chrom', 'start', 'stop', 'i']]
for feat1, feat2 in itertools.combinations_with_replacement(
feature_classes, 2):
feat_values = feature_pair_values(
pairwise_interactions, window_classes, feat1, feat2)
results.append((feat1, feat2, len(feat_values)))
return results
评论列表
文章目录
