Compute the hypergeometric p-value for the given hops counts.
:param total_background_hops: a pandas Series (column of a dataframe)
of total number of hops in the background.
:type total_background_hops: Series[int64]
:param total_experiment_hops: a pandas Series (column of a dataframe)
of total number of hops in the experiment.
:type total_experiment_hops: Series[int64]
:param background_hops: a pandas Series (column of a dataframe)
of number of hops in the background by promoter region.
:type background_hops: Series[int64]
:param experiment_hops: a pandas Series (column of a dataframe)
of number of hops in the experiment by promoter region.
:type experiment_hops: Series[int64]
:return: A pandas Series of length equal to the input Series with the
hypergeometric p-value for each row. If either of the total hop
input Series is 0, the hypergeometric p-value is undefined and
the output Series will have a value of 1 for that row.
:rtype: NDArray[float]
.. note:: This function is vectorized, so it can be applied to
pandas Series (columns of dataframes) to compute the
hypergeometric p-value for each row.
:raises ValueError: If any of the input Series contain negative values,
are not dtype int64 or the input Series are not all the same length.
:Example:
import pandas as pd
total_background_hops = pd.Series([100, 200, 300])
total_experiment_hops = pd.Series([10, 20, 30])
background_hops = pd.Series([5, 10, 15])
experiment_hops = pd.Series([2, 4, 6])
vectorized_hypergeom_pval(
… total_background_hops,
… total_experiment_hops,
… background_hops,
… experiment_hops)
0 0.122360
1 0.027644
2 0.006972
dtype: float64
Source code in callingcardstools/PeakCalling/yeast/hypergeom_pval_vectorized.py
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87 | def hypergeom_pval_vectorized(total_background_hops: Series,
total_experiment_hops: Series,
background_hops: Series,
experiment_hops: Series) -> NDArray:
"""
Compute the hypergeometric p-value for the given hops counts.
:param total_background_hops: a pandas Series (column of a dataframe)
of total number of hops in the background.
:type total_background_hops: Series[int64]
:param total_experiment_hops: a pandas Series (column of a dataframe)
of total number of hops in the experiment.
:type total_experiment_hops: Series[int64]
:param background_hops: a pandas Series (column of a dataframe)
of number of hops in the background by promoter region.
:type background_hops: Series[int64]
:param experiment_hops: a pandas Series (column of a dataframe)
of number of hops in the experiment by promoter region.
:type experiment_hops: Series[int64]
:return: A pandas Series of length equal to the input Series with the
hypergeometric p-value for each row. If either of the `total hop`
input Series is 0, the hypergeometric p-value is undefined and
the output Series will have a value of 1 for that row.
:rtype: NDArray[float]
.. note:: This function is vectorized, so it can be applied to
pandas Series (columns of dataframes) to compute the
hypergeometric p-value for each row.
:raises ValueError: If any of the input Series contain negative values,
are not dtype int64 or the input Series are not all the same length.
:Example:
>>> import pandas as pd
>>> total_background_hops = pd.Series([100, 200, 300])
>>> total_experiment_hops = pd.Series([10, 20, 30])
>>> background_hops = pd.Series([5, 10, 15])
>>> experiment_hops = pd.Series([2, 4, 6])
>>> vectorized_hypergeom_pval(
... total_background_hops,
... total_experiment_hops,
... background_hops,
... experiment_hops)
0 0.122360
1 0.027644
2 0.006972
dtype: float64
"""
# check input
if not len(total_background_hops) == len(total_experiment_hops) == \
len(background_hops) == len(experiment_hops):
raise ValueError('All input Series must be the same length.')
if total_background_hops.min() < 0 \
or total_background_hops.dtype != 'int64':
raise ValueError(('total_background_hops must '
'be a non-negative integer.'))
if total_experiment_hops.min() < 0 \
or total_background_hops.dtype != 'int64':
raise ValueError(('total_experiment_hops must '
'be a non-negative integer'))
if background_hops.min() < 0 or background_hops.dtype != 'int64':
raise ValueError('background_hops must be a non-negative integer')
if experiment_hops.min() < 0 or experiment_hops.dtype != 'int64':
raise ValueError('experiment_hops must be a non-negative integer')
# calculate hypergeometric p-values
M = total_background_hops + total_experiment_hops
n = total_experiment_hops
N = background_hops + experiment_hops
x = experiment_hops - 1
# Handling edge cases
valid = (M >= 1) & (N >= 1)
pval = Series(1, index=total_background_hops.index)
pval[valid] = 1 - hypergeom.cdf(x[valid], M[valid], n[valid], N[valid])
return pval
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