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snp test #1

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dd1250d
snp test
davek44 Sep 8, 2023
ab16b48
add vvcf
davek44 Sep 11, 2023
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1 change: 1 addition & 0 deletions tests/data/snp/.gitignore
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*/
55 changes: 55 additions & 0 deletions tests/data/snp/kidney_eqtl.vcf
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##fileformat=VCFv4.2
chr1 16693559 chr1_16693559_C_T_b38 C T . .
chr1 25235176 chr1_25235176_G_A_b38 G A . .
chr1 121041638 chr1_121041638_C_T_b38 C T . .
chr2 48595242 chr2_48595242_G_A_b38 G A . .
chr2 62506118 chr2_62506118_C_T_b38 C T . .
chr2 74958729 chr2_74958729_G_T_b38 G T . .
chr2 108288574 chr2_108288574_C_T_b38 C T . .
chr2 110607190 chr2_110607190_G_A_b38 G A . .
chr2 127648067 chr2_127648067_C_T_b38 C T . .
chr2 131682500 chr2_131682500_G_C_b38 C G . .
chr2 161244750 chr2_161244750_G_A_b38 G A . .
chr3 69105192 chr3_69105192_C_G_b38 G C . .
chr5 5078200 chr5_5078200_C_T_b38 C T . .
chr5 71044045 chr5_71044045_G_T_b38 G T . .
chr5 181003797 chr5_181003797_T_C_b38 T C . .
chr6 85678170 chr6_85678170_G_T_b38 T G . .
chr6 106629627 chr6_106629627_G_A_b38 G A . .
chr6 111592059 chr6_111592059_C_T_b38 C T . .
chr6 130053316 chr6_130053316_T_A_b38 A T . .
chr6 135302695 chr6_135302695_C_T_b38 C T . .
chr7 30612322 chr7_30612322_G_A_b38 A G . .
chr7 95596945 chr7_95596945_T_C_b38 T C . .
chr9 62885682 chr9_62885682_A_G_b38 A G . .
chr10 45832504 chr10_45832504_G_C_b38 G C . .
chr10 48022458 chr10_48022458_G_A_b38 G A . .
chr10 45947840 chr10_45947840_C_T_b38 C T . .
chr11 1941470 chr11_1941470_C_T_b38 C T . .
chr14 73787509 chr14_73787509_G_A_b38 G A . .
chr14 94129652 chr14_94129652_T_C_b38 T C . .
chr15 95130166 chr15_95130166_G_A_b38 A G . .
chr15 99733402 chr15_99733402_G_C_b38 C G . .
chr15 100574435 chr15_100574435_T_C_b38 T C . .
chr16 1215891 chr16_1215891_G_A_b38 G A . .
chr16 2936322 chr16_2936322_T_C_b38 C T . .
chr16 11976565 chr16_11976565_T_C_b38 C T . .
chr16 14912068 chr16_14912068_C_T_b38 C T . .
chr16 18339330 chr16_18339330_G_C_b38 G C . .
chr16 70251998 chr16_70251998_A_G_b38 A G . .
chr16 84180359 chr16_84180359_G_A_b38 G A . .
chr17 4443684 chr17_4443684_T_C_b38 C T . .
chr17 6700205 chr17_6700205_T_C_b38 T C . .
chr17 15976596 chr17_15976596_C_T_b38 T C . .
chr17 40720995 chr17_40720995_T_C_b38 T C . .
chr17 50547162 chr17_50547162_A_C_b38 A C . .
chr19 44662645 chr19_44662645_A_C_b38 C A . .
chr19 44718265 chr19_44718265_T_C_b38 C T . .
chr19 57438168 chr19_57438168_T_C_b38 C T . .
chr22 17199629 chr22_17199629_A_C_b38 A C . .
chr22 18531430 chr22_18531430_G_A_b38 A G . .
chr22 21468436 chr22_21468436_T_C_b38 C T . .
chr22 21980728 chr22_21980728_T_C_b38 T C . .
chr22 24002141 chr22_24002141_T_G_b38 G T . .
chr22 50562842 chr22_50562842_G_A_b38 G A . .
chrX 153060001 chrX_153060001_G_A_b38 G A . .
92 changes: 92 additions & 0 deletions tests/test_snp.py
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import os
import subprocess

import h5py
import numpy as np
import pandas as pd

from baskerville.dataset import targets_prep_strand

params_file = "tests/data/eval/params.json"
model_file = "tests/data/eval/model.h5"
targets_file = "tests/data/tiny/hg38/targets.txt"
snp_dir = "tests/data/snp"
vcf_file = f"{snp_dir}/kidney_eqtl.vcf"
fasta_file = "%s/assembly/ucsc/hg38.fa" % os.environ["HG38"]
stat_keys = ["logSAD", "logD2"]

def test_snp():
test_out_dir = f"{snp_dir}/full"
scores_file = f"{test_out_dir}/scores.h5"
if os.path.isfile(scores_file):
os.remove(scores_file)

cmd = [
"src/baskerville/scripts/hound_snp.py",
"-f",
fasta_file,
"-o",
test_out_dir,
"--stats",
",".join(stat_keys),
"--rc",
"-t",
targets_file,
params_file,
model_file,
vcf_file
]
print(" ".join(cmd))
subprocess.run(cmd, check=True)

with h5py.File(scores_file, "r") as scores_h5:
for sk in stat_keys:
score = scores_h5[sk][:]
score_var = score.var(axis=0, dtype='float32')
assert (score_var> 0).all()


def test_slice():
test_full_dir = f"{snp_dir}/full"
test_slice_dir = f"{snp_dir}/sub"
os.makedirs(test_slice_dir, exist_ok=True)
scores_file = f"{test_slice_dir}/scores.h5"
if os.path.isfile(scores_file):
os.remove(scores_file)

# slice targets
targets_df = pd.read_csv(targets_file, sep="\t", index_col=0)
rna_mask = np.array([desc.startswith("RNA") for desc in targets_df.description])
targets_rna_df = targets_df[rna_mask]
targets_rna_file = f"{test_slice_dir}/targets_rna.txt"
targets_rna_df.to_csv(targets_rna_file, sep="\t")

cmd = [
"src/baskerville/scripts/hound_snp.py",
"-f",
fasta_file,
"-o",
test_slice_dir,
"--rc",
"--stats",
",".join(stat_keys),
"-t",
targets_rna_file,
params_file,
model_file,
vcf_file
]
print(" ".join(cmd))
subprocess.run(cmd, check=True)

# stranded mask
targets_strand_df = targets_prep_strand(targets_df)
rna_strand_mask = np.array([desc.startswith("RNA") for desc in targets_strand_df.description])

for sk in stat_keys:
with h5py.File(f"{test_full_dir}/scores.h5", "r") as scores_h5:
score_full = scores_h5[sk][:].astype('float32')
score_full = score_full[...,rna_strand_mask]
with h5py.File(f"{test_slice_dir}/scores.h5", "r") as scores_h5:
score_slice = scores_h5[sk][:].astype('float32')
assert np.allclose(score_full, score_slice)
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