def detect_differentially_abundant_features(seqGroup1, seqGroup2, parentSeqGroup1, parentSeqGroup2, coverage, B, preferences, progress): numFeatures = len(seqGroup1) n1 = len(seqGroup1[0]) n2 = len(seqGroup2[0]) # convert to proportions propGroup1 = [] for r in xrange(0, numFeatures): row = [] for c in xrange(0, n1): row.append(float(seqGroup1[r][c]) / parentSeqGroup1[r][c]) propGroup1.append(row) propGroup2 = [] for r in xrange(0, numFeatures): row = [] for c in xrange(0, n2): row.append(float(seqGroup2[r][c]) / parentSeqGroup2[r][c]) propGroup2.append(row) # calculate t-statistics for unpooled variances for each feature T_statistics, effectSizes, notes = calc_twosample_ts( propGroup1, propGroup2) # generate statistics using non-parametric t-test based on permutations of the t-statistic pValuesOneSided, pValuesTwoSided, lowerCIs, upperCIs = permuted_statistics( propGroup1, propGroup2, seqGroup1, seqGroup2, T_statistics, coverage, B, progress) if progress != None and progress.wasCanceled(): return [], [], [], [], [], [] # generate p values for sparse data using fisher's exact test fishers = Fishers(preferences) diffBetweenProp = DiffBetweenPropAsymptoticCC(preferences) for r in xrange(0, numFeatures): if sum(seqGroup1[r]) < n1 and sum(seqGroup2[r]) < n2: p1, p2, note = fishers.hypothesisTest(sum(seqGroup1[r]), sum(seqGroup2[r]), sum(parentSeqGroup1[r]), sum(parentSeqGroup2[r])) l, u, es, note = diffBetweenProp.run(sum(seqGroup1[r]), sum(seqGroup2[r]), sum(parentSeqGroup1[r]), sum(parentSeqGroup2[r]), coverage) pValuesOneSided[r] = p1 pValuesTwoSided[r] = p2 lowerCIs[r] = l upperCIs[r] = u effectSizes[r] = es notes[r] = "heuristic: statistics calculated with Fisher's test" return pValuesOneSided, pValuesTwoSided, lowerCIs, upperCIs, effectSizes, notes
def testDiffBetweenPropAsymptoticCC(self): """Verify computation of Difference between proportions asymptotic CI method with continuity correction""" from plugins.samples.confidenceIntervalMethods.DiffBetweenPropAsymptoticCC import DiffBetweenPropAsymptoticCC diffBetweenPropAsymptoticCC = DiffBetweenPropAsymptoticCC(preferences) lowerCI, upperCI, effectSize, note = diffBetweenPropAsymptoticCC.run( table1[0], table1[1], table1[2], table1[3], 0.95) self.assertAlmostEqual(lowerCI, -13.3167148125733) self.assertAlmostEqual(upperCI, 39.98338147924) self.assertAlmostEqual(effectSize, 13.333333333) lowerCI, upperCI, effectSize, note = diffBetweenPropAsymptoticCC.run( table2[0], table2[1], table2[2], table2[3], 0.95) self.assertAlmostEqual(lowerCI, 0.271407084568653) self.assertAlmostEqual(upperCI, 0.328592915431347) self.assertAlmostEqual(effectSize, 0.3)