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a |
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b/tests/edgepy/test_glm.py |
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1 |
import unittest |
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import numpy as np |
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import pandas as pd |
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from inmoose.edgepy import DGEList, glmFit, glmLRT, glmQLFTest, topTags |
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from inmoose.utils import rnbinom |
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9 |
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class test_DGEGLM(unittest.TestCase): |
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def test_constructor(self): |
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from inmoose.edgepy import DGEGLM |
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d = DGEGLM((1, 2, 3, 4, 5)) |
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self.assertIsNotNone(d.coefficients) |
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self.assertIsNotNone(d.fitted_values) |
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self.assertIsNotNone(d.deviance) |
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self.assertIsNotNone(d.iter) |
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self.assertIsNotNone(d.failed) |
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self.assertIsNone(d.counts) |
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self.assertIsNone(d.design) |
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self.assertIsNone(d.offset) |
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self.assertIsNone(d.dispersion) |
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self.assertIsNone(d.weights) |
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self.assertIsNone(d.prior_count) |
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self.assertIsNone(d.unshrunk_coefficients) |
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self.assertIsNone(d.method) |
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self.assertIsNone(d.AveLogCPM) |
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32 |
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class test_glm(unittest.TestCase): |
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def setUp(self): |
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y = np.array(rnbinom(80, size=5, mu=20, seed=42)).reshape((20, 4)) |
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y = np.vstack(([0, 0, 0, 0], [0, 0, 2, 2], y)) |
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self.group = np.array([1, 1, 2, 2]) |
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self.d = DGEList(counts=y, group=self.group, lib_size=np.arange(1001, 1005)) |
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def test_glmFit(self): |
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with self.assertRaisesRegex( |
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ValueError, expected_regex="No dispersion values found in DGEList object" |
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): |
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self.d.glmFit() |
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# first estimate common dispersion |
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self.d.estimateGLMCommonDisp() |
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# test oneway method |
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e = self.d.glmFit(prior_count=0) |
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self.assertEqual(e.method, "oneway") |
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coef_ref = np.array( |
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[ |
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[-100000000, 0], |
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[-100000000, 99999993.7818981], |
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[-3.818158376, -0.2600061895], |
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[-4.306653048, -0.001994836928], |
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[-3.710751661, -0.5260476658], |
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[-4.511242547, 0.9498904882], |
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[-4.047000403, 0.5031039058], |
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[-3.864988612, 0.2859441576], |
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[-4.235093266, 0.1860767109], |
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[-4.235334586, 0.5821202392], |
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[-3.576947226, -0.4440123944], |
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[-4.04714793, -0.03093068582], |
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[-3.576961828, -0.4441419915], |
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[-3.475280168, -0.7278715951], |
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[-3.888726197, 0.1761259908], |
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[-3.795782156, 0.5221193611], |
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[-3.991513569, -0.2807027773], |
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[-3.559630535, -0.2833225949], |
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[-3.475132503, -0.05155169483], |
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[-3.325699504, -0.1042127818], |
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[-3.888819724, 0.2916754177], |
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[-4.018902851, 0.1281254313], |
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] |
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) |
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self.assertTrue(np.allclose(e.coefficients, coef_ref, atol=1e-6, rtol=0)) |
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self.d.AveLogCPM = None |
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e = self.d.glmFit(prior_count=0) |
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self.assertTrue(np.allclose(e.coefficients, coef_ref, atol=1e-6, rtol=0)) |
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81 |
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# test levenberg method |
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design = np.array([[1, 0], [1, 0], [0, 1], [0, 2]]) |
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e = self.d.glmFit(design=design, prior_count=0) |
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self.assertEqual(e.method, "levenberg") |
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coef_ref = np.array( |
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[ |
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[np.nan, np.nan], |
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[-22.911238587272, -4.33912438169024], |
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[-3.81815837585705, -2.1292021791932], |
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[-4.30665304828008, -2.29993049236002], |
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[-3.71075166096661, -3.34440560955535], |
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[-4.51124254688311, -1.97663680068325], |
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[-4.04700040309538, -2.04478756288351], |
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[-3.86498861235895, -1.99475491866954], |
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[-4.23509326552448, -2.31061055207476], |
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[-4.23533458597804, -1.96831279885414], |
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[-3.57694722597284, -2.1476132892472], |
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[-4.04714792989704, -2.22576713012883], |
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[-3.57696182768343, -2.00991843107384], |
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[-3.47528016753611, -2.43228621567992], |
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[-3.88872619677031, -2.04166124945388], |
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[-3.79578215609008, -1.82484690967382], |
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[-3.99151356863038, -2.36307176428644], |
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[-3.55963053465756, -2.62529213670109], |
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[-3.47513250262264, -2.22250430528328], |
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[-3.32569950353846, -2.03272287830771], |
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[-3.88881972448195, -1.91858334888063], |
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[-4.0189028512534, -2.23681029760319], |
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] |
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) |
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self.assertTrue( |
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np.allclose(e.coefficients, coef_ref, atol=1e-5, rtol=0, equal_nan=True) |
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) |
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115 |
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with self.assertRaisesRegex( |
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ValueError, |
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expected_regex="design should have as many rows as y has columns", |
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): |
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glmFit(self.d.counts, design=np.ones((5, 1))) |
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121 |
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with self.assertRaisesRegex( |
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ValueError, expected_regex="No dispersion values provided" |
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): |
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glmFit(self.d.counts, design=design) |
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126 |
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with self.assertRaisesRegex( |
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ValueError, |
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expected_regex="Dimensions of dispersion do not agree with dimensions of y", |
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): |
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glmFit(self.d.counts, design=design, dispersion=np.ones((5, 1))) |
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132 |
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with self.assertRaisesRegex( |
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ValueError, |
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expected_regex="Dimensions of offset do not agree with dimensions of y", |
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): |
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glmFit( |
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self.d.counts, design=design, dispersion=0.05, offset=np.ones((5, 1)) |
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) |
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140 |
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with self.assertRaisesRegex( |
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ValueError, |
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expected_regex="lib_size has wrong length, should agree with ncol\(y\)", |
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): |
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glmFit( |
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self.d.counts, design=design, dispersion=0.05, lib_size=np.ones((2,)) |
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) |
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148 |
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e = glmFit(self.d.counts, dispersion=0.05, prior_count=0) |
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coef_ref = np.array( |
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[ |
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-1.000000e08, |
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-6.099236e00, |
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-3.120865e00, |
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-3.495951e00, |
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-3.138641e00, |
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-3.114458e00, |
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-2.953059e00, |
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-2.908932e00, |
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-3.325914e00, |
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-3.096400e00, |
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162 |
-2.953421e00, |
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-3.252006e00, |
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-2.951040e00, |
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-2.960371e00, |
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-2.980034e00, |
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-2.691442e00, |
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168 |
-3.309082e00, |
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169 |
-2.895371e00, |
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170 |
-2.686176e00, |
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-2.563754e00, |
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-2.917718e00, |
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-3.142277e00, |
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] |
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).reshape(e.coefficients.shape) |
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self.assertTrue(np.allclose(e.coefficients, coef_ref, atol=1e-6, rtol=0)) |
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177 |
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def test_glmQLFit(self): |
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with self.assertRaisesRegex( |
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ValueError, expected_regex="No dispersion values found in DGEList object" |
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): |
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self.d.glmQLFit() |
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# first estimate common dispersion |
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self.d.estimateGLMCommonDisp() |
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185 |
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e = self.d.glmQLFit() |
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self.assertEqual(e.method, "oneway") |
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coef_ref = np.array( |
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[ |
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[-8.989943, 0.000000000], |
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[-8.989943, 2.832275076], |
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192 |
[-3.812746, -0.258338047], |
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193 |
[-4.297698, -0.001976527], |
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[-3.705921, -0.522525658], |
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[-4.500199, 0.942978065], |
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[-4.040138, 0.500298444], |
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[-3.859316, 0.284480541], |
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198 |
[-4.226767, 0.184626544], |
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199 |
[-4.227016, 0.578352261], |
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200 |
[-3.572745, -0.441539334], |
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201 |
[-4.040290, -0.030706290], |
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202 |
[-3.572760, -0.441672710], |
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203 |
[-3.471510, -0.723582927], |
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[-3.882900, 0.175143962], |
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[-3.790498, 0.519873066], |
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206 |
[-3.985036, -0.278532340], |
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207 |
[-3.555513, -0.281880293], |
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208 |
[-3.471359, -0.051338428], |
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209 |
[-3.322486, -0.103831822], |
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210 |
[-3.882996, 0.290140213], |
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211 |
[-4.012239, 0.127298731], |
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212 |
] |
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213 |
) |
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214 |
self.assertTrue(np.allclose(e.coefficients, coef_ref, atol=1e-4, rtol=0)) |
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215 |
self.assertTrue( |
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216 |
np.array_equal( |
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217 |
[0, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], |
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218 |
e.df_residual_zeros, |
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219 |
) |
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220 |
) |
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221 |
self.assertAlmostEqual(e.df_prior, 4.672744, places=6) |
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222 |
var_post_ref = np.array( |
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223 |
[ |
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224 |
4.458200e-07, |
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225 |
8.302426e-06, |
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226 |
6.524738e-01, |
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227 |
3.709944e-01, |
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228 |
2.159878e00, |
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229 |
6.208389e-01, |
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230 |
5.878879e-01, |
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231 |
1.325040e00, |
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232 |
3.203313e-01, |
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233 |
1.176470e00, |
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234 |
6.941698e-01, |
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235 |
4.743232e-01, |
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236 |
1.205789e00, |
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237 |
5.523578e-01, |
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238 |
6.314750e-01, |
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239 |
5.425240e-01, |
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|
240 |
3.352875e-01, |
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|
241 |
2.129557e00, |
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|
242 |
9.924612e-01, |
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243 |
5.895944e-01, |
|
|
244 |
5.991600e-01, |
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245 |
4.650080e-01, |
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246 |
] |
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247 |
) |
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248 |
self.assertTrue(np.allclose(e.var_post, var_post_ref, atol=1e-6, rtol=0)) |
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249 |
var_prior_ref = np.array( |
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250 |
[ |
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251 |
4.458200e-07, |
|
|
252 |
9.918589e-06, |
|
|
253 |
6.392936e-01, |
|
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254 |
2.804475e-01, |
|
|
255 |
6.394279e-01, |
|
|
256 |
6.503286e-01, |
|
|
257 |
7.568130e-01, |
|
|
258 |
7.713810e-01, |
|
|
259 |
4.405790e-01, |
|
|
260 |
6.716242e-01, |
|
|
261 |
7.527023e-01, |
|
|
262 |
5.182746e-01, |
|
|
263 |
7.526737e-01, |
|
|
264 |
7.527380e-01, |
|
|
265 |
7.426247e-01, |
|
|
266 |
7.491848e-01, |
|
|
267 |
4.566134e-01, |
|
|
268 |
7.744945e-01, |
|
|
269 |
7.490555e-01, |
|
|
270 |
7.042350e-01, |
|
|
271 |
7.668183e-01, |
|
|
272 |
6.273684e-01, |
|
|
273 |
] |
|
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274 |
) |
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275 |
self.assertTrue(np.allclose(e.var_prior, var_prior_ref, atol=1e-6, rtol=0)) |
|
|
276 |
|
|
|
277 |
def test_glmQLFTest(self): |
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278 |
# first estimate common dispersion |
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279 |
self.d.estimateGLMCommonDisp() |
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|
280 |
s = glmQLFTest(self.d.glmQLFit()) |
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281 |
table_ref = pd.DataFrame( |
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|
282 |
{ |
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|
283 |
"log2FoldChange": [ |
|
|
284 |
0.00000, |
|
|
285 |
4.086109e00, |
|
|
286 |
-0.3727030, |
|
|
287 |
-2.851525e-03, |
|
|
288 |
-0.7538452, |
|
|
289 |
1.36042978, |
|
|
290 |
0.7217781, |
|
|
291 |
0.4104187, |
|
|
292 |
0.2663598, |
|
|
293 |
0.8343859, |
|
|
294 |
-0.6370066, |
|
|
295 |
-0.04429981, |
|
|
296 |
-0.6371990, |
|
|
297 |
-1.04390950, |
|
|
298 |
0.2526793, |
|
|
299 |
0.7500183, |
|
|
300 |
-0.4018372, |
|
|
301 |
-0.4066673, |
|
|
302 |
-0.07406570, |
|
|
303 |
-0.14979766, |
|
|
304 |
0.4185838, |
|
|
305 |
0.1836532, |
|
|
306 |
], |
|
|
307 |
"lfcSE": [ |
|
|
308 |
0.00000, |
|
|
309 |
0.119306, |
|
|
310 |
0.308323, |
|
|
311 |
0.339974, |
|
|
312 |
0.312279, |
|
|
313 |
0.323978, |
|
|
314 |
0.299188, |
|
|
315 |
0.293219, |
|
|
316 |
0.324064, |
|
|
317 |
0.310603, |
|
|
318 |
0.298936, |
|
|
319 |
0.316756, |
|
|
320 |
0.298942, |
|
|
321 |
0.304455, |
|
|
322 |
0.297726, |
|
|
323 |
0.284142, |
|
|
324 |
0.323617, |
|
|
325 |
0.291959, |
|
|
326 |
0.280812, |
|
|
327 |
0.275328, |
|
|
328 |
0.294518, |
|
|
329 |
0.308360, |
|
|
330 |
], |
|
|
331 |
"logCPM": [ |
|
|
332 |
10.95644, |
|
|
333 |
1.154123e01, |
|
|
334 |
14.3827970, |
|
|
335 |
1.391051e01, |
|
|
336 |
14.3829969, |
|
|
337 |
14.39896457, |
|
|
338 |
14.6144562, |
|
|
339 |
14.6840429, |
|
|
340 |
14.1263018, |
|
|
341 |
14.4316968, |
|
|
342 |
14.6005670, |
|
|
343 |
14.22314458, |
|
|
344 |
14.6004725, |
|
|
345 |
14.60068510, |
|
|
346 |
14.5711003, |
|
|
347 |
14.9673456, |
|
|
348 |
14.1463618, |
|
|
349 |
14.7114142, |
|
|
350 |
14.96790942, |
|
|
351 |
15.13649877, |
|
|
352 |
14.6566606, |
|
|
353 |
14.3657820, |
|
|
354 |
], |
|
|
355 |
"stat": [ |
|
|
356 |
0.000000e00, |
|
|
357 |
5.998382e05, |
|
|
358 |
4.845263e-01, |
|
|
359 |
4.551851e-05, |
|
|
360 |
5.906064e-01, |
|
|
361 |
6.441791e00, |
|
|
362 |
2.069610e00, |
|
|
363 |
3.032625e-01, |
|
|
364 |
4.811457e-01, |
|
|
365 |
1.334095e00, |
|
|
366 |
1.367280e00, |
|
|
367 |
9.186695e-03, |
|
|
368 |
7.875974e-01, |
|
|
369 |
4.518866e00, |
|
|
370 |
2.379445e-01, |
|
|
371 |
2.538764e00, |
|
|
372 |
1.047358e00, |
|
|
373 |
1.860394e-01, |
|
|
374 |
1.375613e-02, |
|
|
375 |
9.649525e-02, |
|
|
376 |
6.947198e-01, |
|
|
377 |
1.651446e-01, |
|
|
378 |
], |
|
|
379 |
"pvalue": [ |
|
|
380 |
1.00000000, |
|
|
381 |
0.01861634, |
|
|
382 |
0.50988854, |
|
|
383 |
0.99481417, |
|
|
384 |
0.46850481, |
|
|
385 |
0.04035278, |
|
|
386 |
0.19546361, |
|
|
387 |
0.59978572, |
|
|
388 |
0.51131874, |
|
|
389 |
0.28775816, |
|
|
390 |
0.28234748, |
|
|
391 |
0.92645653, |
|
|
392 |
0.40567730, |
|
|
393 |
0.07303738, |
|
|
394 |
0.64131120, |
|
|
395 |
0.15720197, |
|
|
396 |
0.34177834, |
|
|
397 |
0.67982404, |
|
|
398 |
0.91008476, |
|
|
399 |
0.76555392, |
|
|
400 |
0.43338141, |
|
|
401 |
0.69717923, |
|
|
402 |
], |
|
|
403 |
}, |
|
|
404 |
index=[f"gene{i}" for i in range(22)], |
|
|
405 |
) |
|
|
406 |
pd.testing.assert_frame_equal(table_ref, s, check_frame_type=False, rtol=1e-4) |
|
|
407 |
|
|
|
408 |
def test_glmLRT(self): |
|
|
409 |
# first estimate common dispersion |
|
|
410 |
self.d.estimateGLMCommonDisp() |
|
|
411 |
s = glmLRT(self.d.glmFit()) |
|
|
412 |
table_ref = pd.DataFrame( |
|
|
413 |
{ |
|
|
414 |
"log2FoldChange": [ |
|
|
415 |
0.00000, |
|
|
416 |
4.08610921, |
|
|
417 |
-0.3727030, |
|
|
418 |
-2.851525e-03, |
|
|
419 |
-0.7538452, |
|
|
420 |
1.36042978, |
|
|
421 |
0.7217781, |
|
|
422 |
0.4104187, |
|
|
423 |
0.2663598, |
|
|
424 |
0.8343859, |
|
|
425 |
-0.6370066, |
|
|
426 |
-0.044299812, |
|
|
427 |
-0.6371990, |
|
|
428 |
-1.043910, |
|
|
429 |
0.2526793, |
|
|
430 |
0.7500183, |
|
|
431 |
-0.4018372, |
|
|
432 |
-0.4066673, |
|
|
433 |
-0.07406570, |
|
|
434 |
-0.1497977, |
|
|
435 |
0.4185838, |
|
|
436 |
0.18365325, |
|
|
437 |
], |
|
|
438 |
"lfcSE": [ |
|
|
439 |
0.00000, |
|
|
440 |
0.119306, |
|
|
441 |
0.308323, |
|
|
442 |
0.339974, |
|
|
443 |
0.312279, |
|
|
444 |
0.323978, |
|
|
445 |
0.299188, |
|
|
446 |
0.293219, |
|
|
447 |
0.324064, |
|
|
448 |
0.310603, |
|
|
449 |
0.298936, |
|
|
450 |
0.316756, |
|
|
451 |
0.298942, |
|
|
452 |
0.304455, |
|
|
453 |
0.297726, |
|
|
454 |
0.284142, |
|
|
455 |
0.323617, |
|
|
456 |
0.291959, |
|
|
457 |
0.280812, |
|
|
458 |
0.275328, |
|
|
459 |
0.294518, |
|
|
460 |
0.308360, |
|
|
461 |
], |
|
|
462 |
"logCPM": [ |
|
|
463 |
10.95644, |
|
|
464 |
11.54122852, |
|
|
465 |
14.3827970, |
|
|
466 |
1.391051e01, |
|
|
467 |
14.3829969, |
|
|
468 |
14.39896457, |
|
|
469 |
14.6144562, |
|
|
470 |
14.6840429, |
|
|
471 |
14.1263018, |
|
|
472 |
14.4316968, |
|
|
473 |
14.6005670, |
|
|
474 |
14.223144579, |
|
|
475 |
14.6004725, |
|
|
476 |
14.600685, |
|
|
477 |
14.5711003, |
|
|
478 |
14.9673456, |
|
|
479 |
14.1463618, |
|
|
480 |
14.7114142, |
|
|
481 |
14.96790942, |
|
|
482 |
15.1364988, |
|
|
483 |
14.6566606, |
|
|
484 |
14.36578202, |
|
|
485 |
], |
|
|
486 |
"stat": [ |
|
|
487 |
0.000000e00, |
|
|
488 |
4.980113e00, |
|
|
489 |
3.161407e-01, |
|
|
490 |
1.688711e-05, |
|
|
491 |
1.275638e00, |
|
|
492 |
3.999315e00, |
|
|
493 |
1.216698e00, |
|
|
494 |
4.018350e-01, |
|
|
495 |
1.541260e-01, |
|
|
496 |
1.569523e00, |
|
|
497 |
9.491243e-01, |
|
|
498 |
4.357462e-03, |
|
|
499 |
9.496767e-01, |
|
|
500 |
2.496031e00, |
|
|
501 |
1.502560e-01, |
|
|
502 |
1.377340e00, |
|
|
503 |
3.511659e-01, |
|
|
504 |
3.961817e-01, |
|
|
505 |
1.365242e-02, |
|
|
506 |
5.689306e-02, |
|
|
507 |
4.162483e-01, |
|
|
508 |
7.679356e-02, |
|
|
509 |
], |
|
|
510 |
"pvalue": [ |
|
|
511 |
1.00000000, |
|
|
512 |
0.02564032, |
|
|
513 |
0.57393623, |
|
|
514 |
0.99672119, |
|
|
515 |
0.25871164, |
|
|
516 |
0.04551876, |
|
|
517 |
0.27000955, |
|
|
518 |
0.52614310, |
|
|
519 |
0.69462318, |
|
|
520 |
0.21027629, |
|
|
521 |
0.32994229, |
|
|
522 |
0.94736901, |
|
|
523 |
0.32980161, |
|
|
524 |
0.11413364, |
|
|
525 |
0.69829083, |
|
|
526 |
0.24055469, |
|
|
527 |
0.55345388, |
|
|
528 |
0.52906782, |
|
|
529 |
0.90698400, |
|
|
530 |
0.81147574, |
|
|
531 |
0.51881503, |
|
|
532 |
0.78169064, |
|
|
533 |
], |
|
|
534 |
}, |
|
|
535 |
index=[f"gene{i}" for i in range(22)], |
|
|
536 |
) |
|
|
537 |
pd.testing.assert_frame_equal(table_ref, s, check_frame_type=False, rtol=1e-4) |
|
|
538 |
|
|
|
539 |
def test_topTags(self): |
|
|
540 |
self.d.estimateGLMCommonDisp() |
|
|
541 |
s = glmLRT(self.d.glmFit()) |
|
|
542 |
t = topTags(s) |
|
|
543 |
self.assertTrue( |
|
|
544 |
np.array_equal( |
|
|
545 |
t.table.index, |
|
|
546 |
[ |
|
|
547 |
"gene1", |
|
|
548 |
"gene5", |
|
|
549 |
"gene13", |
|
|
550 |
"gene9", |
|
|
551 |
"gene15", |
|
|
552 |
"gene4", |
|
|
553 |
"gene6", |
|
|
554 |
"gene12", |
|
|
555 |
"gene10", |
|
|
556 |
"gene20", |
|
|
557 |
], |
|
|
558 |
) |
|
|
559 |
) |