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/tests/diffexp/test_meta.py
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--- a +++ b/tests/diffexp/test_meta.py @@ -0,0 +1,939 @@ +import unittest + +import numpy as np +import pandas as pd +import patsy + +from inmoose.deseq2 import DESeq, makeExampleDESeqDataSet +from inmoose.diffexp import DEResults, meta_de +from inmoose.edgepy import ( + DGEList, + exactTest, + glmLRT, + glmQLFTest, +) +from inmoose.utils import Factor + + +class Test(unittest.TestCase): + def test_meta_de(self): + # test contrasts + dds = makeExampleDESeqDataSet(n=200, m=12, seed=42) + dds.obs["condition"] = Factor(np.repeat([1, 2, 3], 4)) + dds.obs["group"] = Factor(np.repeat([[1, 2]], 6, axis=0).flatten()) + dds.counts()[:, 0] = np.repeat([100, 200, 800], 4) + + dds.design = "~ group + condition" + + # DE with deseq + dds = DESeq(dds) + deseq_res = dds.results() + + # DE with edgepy + d = DGEList(counts=dds.X.T, group=dds.obs["group"]) + d.design = patsy.dmatrix("~ group + condition", data=dds.obs) + d = d.estimateGLMCommonDisp() + + # DE with edgepy (LRT) + edgepy_lrt_res = glmLRT(d.glmFit()) + + # DE with edgepy (QL FTest) + edgepy_ql_res = glmQLFTest(d.glmQLFit()) + + # DE with edgepy (exactTest) + edgepy_et_res = exactTest(d) + + # meta-analysis + des = [deseq_res, edgepy_lrt_res, edgepy_ql_res, edgepy_et_res] + res = meta_de(des) + + # combined logFC and confidence intervals are controlled against metafor + # adjusted p-values have no external control, and are tested for non-regression + ref = pd.DataFrame( + { + "combined logFC (CI_L)": [ + 0.6660151964096146, + -2.6386163106710177, + -2.4294256191695074, + -0.6488512767501585, + -2.91706443340857, + -3.521554387539502, + -0.013715570856962955, + -2.44433271322974, + -3.0287062069792716, + -1.2632361321964904, + -0.4565657144319961, + -1.050526185309593, + -2.898567141180949, + -0.5981094405745551, + 0.542876997367387, + -2.5842836601101418, + -1.4881728468777093, + 0.2816630199971988, + -1.621914242625586, + -2.175531983553664, + -2.426621491203676, + -2.2862239530712416, + 0.6061652115075944, + 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"log2FoldChange": [2.0, 3.0, 4.0], + "lfcSE": [0.5, 0.5, 0.5], + "pvalue": [0.1, 0.15, 0.2], + }, + index=idx, + ) + res3 = DEResults( + { + "log2FoldChange": [3.0, np.nan, 5.0], + "lfcSE": [0.5, np.nan, 0.5], + "pvalue": [0.2, np.nan, 0.1], + }, + index=idx, + ) + meta1 = meta_de([res1, res2, res3]) + meta2 = meta_de([res1, res3]) + ref1 = pd.DataFrame( + { + "combined logFC (CI_L)": [ + 0.8684142716330685, + np.nan, + 2.868414271633068, + ], + "combined logFC (CI_R)": [ + 3.1315857283669315, + np.nan, + 5.131585728366931, + ], + "combined logFC": [2, np.nan, 4], + "adjusted combined pval": [ + 0.13058835750961412, + 0.15, + 0.13058835750961412, + ], + }, + index=idx, + ) + ref2 = pd.DataFrame( + { + "combined logFC (CI_L)": [ + 0.0400360524639638, + np.nan, + 2.0400360524639636, + ], + "combined logFC (CI_R)": [ + 3.9599639475360364, + np.nan, + 5.959963947536036, + ], + "combined logFC": [2, np.nan, 4], + "adjusted combined pval": [ + 0.09824046010856295, + np.nan, + 0.09824046010856295, + ], + }, + index=idx, + ) + pd.testing.assert_frame_equal(meta1, ref1) + pd.testing.assert_frame_equal(meta2, ref2)