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CoNekT/utils/enrichment.py
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from math import log, exp | |
from mpmath import loggamma | |
def logchoose(ni, ki): | |
try: | |
lgn1 = loggamma(ni+1) | |
lgk1 = loggamma(ki+1) | |
lgnk1 = loggamma(ni-ki+1) | |
except ValueError: | |
#print ni,ki | |
raise ValueError | |
return lgn1 - (lgnk1 + lgk1) | |
def gauss_hypergeom(X, n, m, N): | |
assert N >= m, 'Number of items %i must be larger than the number of marked items %i' % (N, m) | |
assert m >= X, 'Number of marked items %i must be larger than the number of sucesses %i' % (m, X) | |
assert n >= X, 'Number of draws %i must be larger than the number of sucesses %i' % (n, X) | |
assert N >= n, 'Number of draws %i must be smaller than the total number of items %i' % (n, N) | |
r1 = logchoose(m, X) | |
try: | |
r2 = logchoose(N-m, n-X) | |
except ValueError: | |
return 0 | |
r3 = logchoose(N, n) | |
return exp(r1 + r2 - r3) | |
def hypergeo_cdf(X, n, m, N): | |
""" | |
Returns the cummulative distribution function of drawing X successes of m marked items | |
in n draws from a bin of N total items. | |
:param X: number of successful draws | |
:param n: number of draws | |
:param m: number of marked items | |
:param N: total number of items | |
:return: cummulative distribution function | |
""" | |
assert N >= m, 'Number of items %i must be larger than the number of marked items %i' % (N, m) | |
assert m >= X, 'Number of marked items %i must be larger than the number of sucesses %i' % (m, X) | |
assert n >= X, 'Number of draws %i must be larger than the number of sucesses %i' % (n, X) | |
assert N >= n, 'Number of draws %i must be smaller than the total number of items %i' % (n, N) | |
assert N-m >= n-X, 'There are more failures %i than unmarked items %i' % (N-m, n-X) | |
s = 0 | |
for i in range(0, X+1): | |
s += max(gauss_hypergeom(i, n, m, N), 0.0) | |
return min(max(s, 0.0), 1) | |
def hypergeo_sf(X, n, m, N): | |
""" | |
Returns the significance of drawing X successes of m marked items | |
in n draws from a bin of N total items. | |
:param X: number of successful draws | |
:param n: number of draws | |
:param m: number of marked items | |
:param N: total number of items | |
:return: significance | |
""" | |
assert N >= m, 'Number of items %i must be larger than the number of marked items %i' % (N, m) | |
assert m >= X, 'Number of marked items %i must be larger than the number of sucesses %i' % (m, X) | |
assert n >= X, 'Number of draws %i must be larger than the number of sucesses %i' % (n, X) | |
assert N >= n, 'Number of draws %i must be smaller than the total number of items %i' % (n, N) | |
assert N-m >= n-X, 'There are more failures %i than unmarked items %i' % (N-m, n-X) | |
s = 0 | |
for i in range(X, min(m, n)+1): | |
s += max(gauss_hypergeom(i, n, m, N), 0.0) | |
return min(max(s, 0.0), 1) | |
def rank_simple(vector): | |
return sorted(range(len(vector)), key=vector.__getitem__) | |
def rankdata(a, method='average'): | |
""" | |
source: http://stackoverflow.com/questions/3071415/efficient-method-to-calculate-the-rank-vector-of-a-list-in-python | |
:param a: | |
:return: | |
""" | |
n = len(a) | |
ivec=rank_simple(a) | |
svec=[a[rank] for rank in ivec] | |
sumranks = 0 | |
dupcount = 0 | |
newarray = [0]*n | |
for i in range(n): | |
sumranks += i | |
dupcount += 1 | |
if i == n-1 or svec[i] != svec[i+1]: | |
for j in range(i-dupcount+1,i+1): | |
if method == 'average': | |
averank = sumranks / float(dupcount) + 1 | |
newarray[ivec[j]] = averank | |
elif method == 'max': | |
newarray[ivec[j]] = i+1 | |
elif method == 'min': | |
newarray[ivec[j]] = i+1 -dupcount+1 | |
else: | |
raise NameError('Unsupported method') | |
sumranks = 0 | |
dupcount = 0 | |
return newarray | |
def fdr_correction(a): | |
""" | |
applies fdr correction to a list of p-values | |
:param a: list of p-values | |
:return: list with adjusted/corrected p-values | |
""" | |
ranks = rankdata(a, method='max') | |
output = [] | |
for p, rank in zip(a, ranks): | |
corrected = p * (len(a)/rank) | |
output.append(corrected if corrected < max(a) else max(a)) | |
return output |