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,
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],
"combined logFC (CI_R)": [
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0.35083068561892405,
1.2758758057915247,
0.987419819649228,
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"adjusted combined pval": [
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],
},
index=[f"gene{i}" for i in range(200)],
)
pd.testing.assert_frame_equal(res, ref, rtol=1e-4)
def test_meta_de_nan(self):
"""check robustness of meta_de to NaN values"""
idx = ["gene1", "gene2", "gene3"]
res1 = DEResults(
{
"log2FoldChange": [1.0, np.nan, 3.0],
"lfcSE": [0.5, np.nan, 0.5],
"pvalue": [0.1, np.nan, 0.2],
},
index=idx,
)
res2 = DEResults(
{
"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)
Datasets
Models
