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rename X_approx_distribution to X_approximate_distribution (#2337)

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Bruce Martin
2021-07-27 13:43:04 -07:00
committed by GitHub
parent 1998c0ad63
commit 0b1ab02a60
20 changed files with 79 additions and 77 deletions
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backend/common/compute/diffexp_generic.py
+7 -7
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@@ -1,6 +1,6 @@
import numpy as np
from scipy import sparse, stats
from backend.common.constants import XApproxDistribution
from backend.common.constants import XApproximateDistribution
def diffexp_ttest(adaptor, maskA, maskB, top_n=8, diffexp_lfc_cutoff=0.01):
@@ -30,13 +30,13 @@ def diffexp_ttest(adaptor, maskA, maskB, top_n=8, diffexp_lfc_cutoff=0.01):
:return: for top N genes, {"positive": for top N genes, [ varindex, foldchange, pval, pval_adj ], "negative": for top N genes, [ varindex, foldchange, pval, pval_adj ]}
"""
X_approx_distribution = adaptor.get_X_approx_distribution()
X_approximate_distribution = adaptor.get_X_approximate_distribution()
dataA = adaptor.get_X_array(maskA, None)
dataB = adaptor.get_X_array(maskB, None)
# mean, variance, N - calculate for both selections
meanA, vA, nA = mean_var_n(dataA, X_approx_distribution)
meanB, vB, nB = mean_var_n(dataB, X_approx_distribution)
meanA, vA, nA = mean_var_n(dataA, X_approximate_distribution)
meanB, vB, nB = mean_var_n(dataB, X_approximate_distribution)
res = diffexp_ttest_from_mean_var(meanA, vA, nA, meanB, vB, nB, top_n, diffexp_lfc_cutoff)
return res
@@ -113,7 +113,7 @@ def diffexp_ttest_from_mean_var(meanA, varA, nA, meanB, varB, nB, top_n, diffexp
# Convenience function which handles sparse data
def mean_var_n(X, X_approx_distribution=XApproxDistribution.NORMAL):
def mean_var_n(X, X_approximate_distribution=XApproximateDistribution.NORMAL):
"""
Two-pass variance calculation. Numerically (more) stable
than naive methods (and same method used by numpy.var())
@@ -131,14 +131,14 @@ def mean_var_n(X, X_approx_distribution=XApproxDistribution.NORMAL):
with np.errstate(divide="call", invalid="call", call=fp_err_set):
n = X.shape[0]
if sparse.issparse(X):
if X_approx_distribution == XApproxDistribution.COUNT:
if X_approximate_distribution == XApproximateDistribution.COUNT:
X = X.log1p()
mean = X.mean(axis=0).A1
dfm = X - mean
sumsq = np.sum(np.multiply(dfm, dfm), axis=0).A1
v = sumsq / (n - 1)
else:
if X_approx_distribution == XApproxDistribution.COUNT:
if X_approximate_distribution == XApproximateDistribution.COUNT:
X = np.log1p(X)
mean = X.mean(axis=0)
dfm = X - mean