# done the above on pca_RF jupyter notebook
# mt = hl.read_matrix_table(
# f"{temp_dir}/ddd-elgh-ukbb/Sanger_cohorts_chr1-20-XY_new_cohorts.mt")
#mt = hl.split_multi_hts( mt, keep_star=False, left_aligned=False)
mt.write(
f"{tmp_dir}/ddd-elgh-ukbb/Sanger_chr1-20-XY_new_cohorts_split_multi_pops.mt",
overwrite=True)
# filter matrixtable
logger.info("wrote mt ")
# filter mt
# mt = hl.read_matrix_table(
# f"{temp_dir}/ddd-elgh-ukbb/Sanger_cohorts_chr1-20-XY_new_cohorts_split_multi.mt")
mt = mt.filter_rows(hl.is_snp(mt.alleles[0], mt.alleles[1]))
mt = mt.filter_rows(~hl.is_mnp(mt.alleles[0], mt.alleles[1]))
mt = mt.filter_rows(~hl.is_indel(mt.alleles[0], mt.alleles[1]))
mt = mt.filter_rows(~hl.is_complex(mt.alleles[0], mt.alleles[1]))
mt_vqc = hl.variant_qc(mt, name='variant_QC_Hail')
# (mt_vqc.variant_QC_Hail.p_value_hwe >= 10 ** -6) & not to use this according to hcm.
mt_vqc_filtered = mt_vqc.filter_rows(
(mt_vqc.variant_QC_Hail.call_rate >= 0.99)
& (mt_vqc.variant_QC_Hail.AF[1] >= 0.05)
& (mt_vqc.variant_QC_Hail.AF[1] <= 0.95))
mt_vqc_filtered = mt_vqc_filtered.filter_rows(hl.is_defined(
bed_to_exclude_pca[mt_vqc_filtered.locus]),
keep=False)
# overlap AKT dataset:
mt_1kg_chr1_chr20 = hl.read_matrix_table(
f"{temp_dir}/ddd-elgh-ukbb/relatedness_ancestry/ancestry_work/1000g_chr1_20_AKT_overlap.mt"
)
#mt_vqc_filtered1 = mt_vqc_filtered.key_rows_by("locus")
def main(args):
bed_to_exclude_pca = hl.import_bed(locations_exclude_from_pca,
reference_genome='GRCh38')
cohorts_pop = hl.import_table(cohorts_populations,
delimiter="\t").key_by('s')
# # overlap AKT dataset
overlap_1kg_AKT = hl.import_matrix_table(AKT_overlap)
# drop cohorts
# annotate with cohorts and populations from s3 table.
# save matrixtable
mt = hl.read_matrix_table(args.matrixtable)
mt = mt.annotate_cols(cohort=cohorts_pop[mt.s].cohort)
mt = mt.annotate_cols(original_pop=cohorts_pop[mt.s].known_population)
mt = mt.annotate_cols(known_pop=cohorts_pop[mt.s].known_population_updated)
# mt = mt.annotate_cols(superpopulation=cohorts_pop[mt.s].superpopulation)
mt = mt.annotate_cols(gVCF=cohorts_pop[mt.s].gVCF_ID)
mt.write(
f"{args.output_dir}/ddd-elgh-ukbb/Sanger_chr1-20-XY_new_cohorts_split_multi_pops.mt",
overwrite=True)
# filter matrixtable
logger.info("wrote mt ")
# filter mt
mt = mt.filter_rows(hl.is_snp(mt.alleles[0], mt.alleles[1]))
mt = mt.filter_rows(~hl.is_mnp(mt.alleles[0], mt.alleles[1]))
mt = mt.filter_rows(~hl.is_indel(mt.alleles[0], mt.alleles[1]))
mt = mt.filter_rows(~hl.is_complex(mt.alleles[0], mt.alleles[1]))
mt_vqc = hl.variant_qc(mt, name='variant_QC_Hail')
# (mt_vqc.variant_QC_Hail.p_value_hwe >= 10 ** -6) & not to use this according to hcm.
mt_vqc_filtered = mt_vqc.filter_rows(
(mt_vqc.variant_QC_Hail.call_rate >= 0.99)
& (mt_vqc.variant_QC_Hail.AF[1] >= 0.05)
& (mt_vqc.variant_QC_Hail.AF[1] <= 0.95))
mt_vqc_filtered = mt_vqc_filtered.filter_rows(hl.is_defined(
bed_to_exclude_pca[mt_vqc_filtered.locus]),
keep=False)
# overlap AKT dataset:
# overlap_1kg_AKT
# mt_1kg_chr1_chr20 = hl.read_matrix_table(
# f"{temp_dir}/ddd-elgh-ukbb/relatedness_ancestry/ancestry_work/1000g_chr1_20_AKT_overlap.mt")
overlap_1kg_AKT = overlap_1kg_AKT.key_rows_by("locus")
mt_vqc_filtered = mt_vqc_filtered.filter_rows(
hl.is_defined(overlap_1kg_AKT.rows()[mt_vqc_filtered.locus]))
logger.info("done filtering writing mt")
# ld pruning
pruned_ht = hl.ld_prune(mt_vqc_filtered.GT, r2=0.2, bp_window_size=500000)
#pruned_ht = hl.ld_prune(mt.GT, r2=0.1)
pruned_mt = mt_vqc_filtered.filter_rows(
hl.is_defined(pruned_ht[mt_vqc_filtered.row_key]))
# remove pruned areas that need to be removed
# autosomes only:
pruned_mt = pruned_mt.filter_rows(pruned_mt.locus.in_autosome())
pruned_mt.write(
f"{args.output_dir}/ddd-elgh-ukbb/chr1_chr20_ldpruned_updated.mt",
overwrite=True)
# pruned_mt = hl.read_matrix_table(
# f"{temp_dir}/ddd-elgh-ukbb/relatedness_ancestry/ddd-elgh-ukbb/chr1_chr20_XY_ldpruned.mt")
# related_samples_to_drop = hl.read_table(
# f"{temp_dir}/ddd-elgh-ukbb/relatedness_ancestry/ddd-elgh-ukbb/chr1_chr20_XY_related_samples_to_remove.ht")
logger.info("run_pca_with_relateds")
# pca_evals, pca_scores, pca_loadings = run_pca_with_relateds(
# pruned_mt, related_samples_to_drop, autosomes_only=True)
pca_evals, pca_scores, pca_loadings = hl.hwe_normalized_pca(
pruned_mt.GT, k=10, compute_loadings=True)
pca_scores = pca_scores.annotate(
known_pop=pruned_mt.cols()[pca_scores.s].known_pop)
# mt = mt.annotate_cols(
# loadings=pca_loadings[mt_vqc_filtered.col_key].loadings)
# mt = mt.annotate_cols(known_pop="unk")
# pca_scores = pca_scores.annotate(known_pop="unk")
pca_scores.write(
f"{args.output_dir}/ddd-elgh-ukbb/pca_scores_after_pruning.ht",
overwrite=True)
pca_loadings.write(
f"{args.output_dir}/ddd-elgh-ukbb/pca_loadings_after_pruning.ht",
overwrite=True)
with open(f"{args.output_dir}/ddd-elgh-ukbb/pca_evals_after_pruning.txt",
'w') as f:
for val in pca_evals:
f.write(str(val))
logger.info("assign population pcs")
pop_ht, pop_clf = assign_population_pcs(pca_scores,
pca_scores.scores,
known_col="known_pop",
n_estimators=100,
prop_train=0.8,
min_prob=0.5)
pop_ht.write(f"{args.output_dir}/ddd-elgh-ukbb/pop_assignments_updated.ht",
overwrite=True)
pop_ht.export(
f"{args.output_dir}/ddd-elgh-ukbb/pop_assignments_updated.tsv.gz")
