def query(): # pylint: disable=too-many-locals
"""Query script entry point."""
hl.init(default_reference='GRCh38')
tob_wgs = hl.read_matrix_table(TOB_WGS).key_rows_by('locus', 'alleles')
snp_chip = hl.read_matrix_table(SNP_CHIP).key_rows_by('locus', 'alleles')
# filter to loci that are contained in snp-chip data after densifying
tob_wgs = hl.experimental.densify(tob_wgs)
tob_wgs = tob_wgs.select_entries(
GT=lgt_to_gt(tob_wgs.LGT, tob_wgs.LA)
).select_cols()
snp_chip = snp_chip.select_entries(snp_chip.GT).select_cols()
snp_chip = snp_chip.key_cols_by(s=snp_chip.s + '_snp_chip')
tob_combined = tob_wgs.union_cols(snp_chip)
tob_combined = tob_combined.cache()
print(tob_combined.count_rows())
# Perform PCA
eigenvalues_path = output_path('eigenvalues.ht')
scores_path = output_path('scores.ht')
loadings_path = output_path('loadings.ht')
eigenvalues, scores, loadings = hl.hwe_normalized_pca(
tob_combined.GT, compute_loadings=True, k=20
)
hl.Table.from_pandas(pd.DataFrame(eigenvalues)).export(eigenvalues_path)
scores.write(scores_path, overwrite=True)
loadings.write(loadings_path, overwrite=True)
def query():
"""Query script entry point."""
hl.init(default_reference='GRCh38')
tob_wgs = hl.read_matrix_table(TOB_WGS)
hgdp_1kg = hl.read_matrix_table(GNOMAD_HGDP_1KG_MT)
# keep loci that are contained in the densified, filtered tob-wgs mt
hgdp_1kg = hgdp_1kg.semi_join_rows(tob_wgs.rows())
# Entries and columns must be identical
tob_wgs_select = tob_wgs.select_entries(
GT=lgt_to_gt(tob_wgs.LGT, tob_wgs.LA)).select_cols()
hgdp_1kg_select = hgdp_1kg.select_entries(hgdp_1kg.GT).select_cols()
# Join datasets
hgdp1kg_tobwgs_joined = hgdp_1kg_select.union_cols(tob_wgs_select)
# Add in metadata information
hgdp_1kg_metadata = hgdp_1kg.cols()
hgdp1kg_tobwgs_joined = hgdp1kg_tobwgs_joined.annotate_cols(
hgdp_1kg_metadata=hgdp_1kg_metadata[hgdp1kg_tobwgs_joined.s])
# save this for population-level PCAs
mt_path = output_path('hgdp1kg_tobwgs_joined_all_samples.mt')
if not hl.hadoop_exists(mt_path):
hgdp1kg_tobwgs_joined.write(mt_path)
# Perform PCA
eigenvalues_path = output_path('eigenvalues.ht')
scores_path = output_path('scores.ht')
loadings_path = output_path('loadings.ht')
eigenvalues, scores, loadings = hl.hwe_normalized_pca(
hgdp1kg_tobwgs_joined.GT, compute_loadings=True, k=20)
hl.Table.from_pandas(pd.DataFrame(eigenvalues)).export(eigenvalues_path)
scores.write(scores_path, overwrite=True)
loadings.write(loadings_path, overwrite=True)
def query(output, pop): # pylint: disable=too-many-locals
"""Query script entry point."""
hl.init(default_reference='GRCh38')
mt = hl.read_matrix_table(HGDP1KG_TOBWGS)
if pop:
# Get samples from the specified population only
mt = mt.filter_cols((
mt.hgdp_1kg_metadata.population_inference.pop == pop.lower())
| (mt.s.contains('TOB')))
else:
mt = mt.filter_cols(mt.s.contains('TOB'))
# Perform PCA
eigenvalues_path = f'{output}/eigenvalues.csv'
scores_path = f'{output}/scores.ht'
loadings_path = f'{output}/loadings.ht'
eigenvalues, scores, loadings = hl.hwe_normalized_pca(
mt.GT, compute_loadings=True, k=20)
eigenvalues_df = pd.DataFrame(eigenvalues)
eigenvalues_df.to_csv(eigenvalues_path, index=False)
scores.write(scores_path, overwrite=True)
loadings.write(loadings_path, overwrite=True)
# get TOB-WGS allele frequencies
tob_wgs = hl.read_matrix_table(TOB_WGS).key_rows_by('locus', 'alleles')
tob_wgs = tob_wgs.annotate_entries(GT=lgt_to_gt(tob_wgs.LGT, tob_wgs.LA))
tob_wgs = tob_wgs.annotate_rows(
gt_stats=hl.agg.call_stats(tob_wgs.GT, tob_wgs.alleles))
# Get gnomAD allele frequency of variants that aren't in TOB-WGS
loadings_gnomad = hl.read_table(GNOMAD_LIFTOVER_LOADINGS).key_by(
'locus', 'alleles')
hgdp_1kg = hl.read_matrix_table(GNOMAD_HGDP_1KG_MT)
hgdp_1kg_row = hgdp_1kg.rows()[loadings_gnomad.locus,
loadings_gnomad.alleles]
tob_wgs_row = tob_wgs.rows()[loadings_gnomad.locus,
loadings_gnomad.alleles]
loadings_gnomad = loadings_gnomad.annotate(
gnomad_AF=hgdp_1kg_row.gnomad_freq.AF,
gnomad_popmax_AF=hgdp_1kg_row.gnomad_popmax.AF,
TOB_WGS_AF=tob_wgs_row.gt_stats.AF,
)
population_af_metadata = hgdp_1kg.gnomad_freq_meta.collect()
loadings_gnomad = loadings_gnomad.annotate_globals(
gnomad_freq_meta=population_af_metadata)
gnomad_variants = loadings_gnomad.drop('loadings')
gnomad_variants_path = f'{output}/gnomad_annotated_variants.mt'
gnomad_variants.write(gnomad_variants_path)
def query(output): # pylint: disable=too-many-locals
"""Query script entry point."""
hl.init(default_reference='GRCh38')
hgdp_1kg = hl.read_matrix_table(GNOMAD_HGDP_1KG_MT)
tob_wgs = hl.read_matrix_table(TOB_WGS).key_rows_by('locus', 'alleles')
# filter to loci that are contained in both matrix tables after densifying
tob_wgs = hl.experimental.densify(tob_wgs)
# Entries and columns must be identical
tob_wgs_select = tob_wgs.select_entries(
GT=lgt_to_gt(tob_wgs.LGT, tob_wgs.LA))
hgdp_1kg_select = hgdp_1kg.select_entries(hgdp_1kg.GT)
hgdp_1kg_select = hgdp_1kg_select.select_cols()
# Join datasets
hgdp1kg_tobwgs_joined = hgdp_1kg_select.union_cols(tob_wgs_select)
# Add in metadata information
hgdp_1kg_metadata = hgdp_1kg.cols()
hgdp1kg_tobwgs_joined = hgdp1kg_tobwgs_joined.annotate_cols(
hgdp_1kg_metadata=hgdp_1kg_metadata[hgdp1kg_tobwgs_joined.s])
# choose variants based off of gnomAD v3 parameters
hgdp1kg_tobwgs_joined = hl.variant_qc(hgdp1kg_tobwgs_joined)
hgdp1kg_tobwgs_joined = hgdp1kg_tobwgs_joined.annotate_rows(
IB=hl.agg.inbreeding(hgdp1kg_tobwgs_joined.GT,
hgdp1kg_tobwgs_joined.variant_qc.AF[1]))
hgdp1kg_tobwgs_joined = hgdp1kg_tobwgs_joined.filter_rows(
(hl.len(hgdp1kg_tobwgs_joined.alleles) == 2)
& (hgdp1kg_tobwgs_joined.locus.in_autosome())
& (hgdp1kg_tobwgs_joined.variant_qc.AF[1] > 0.01)
& (hgdp1kg_tobwgs_joined.variant_qc.call_rate > 0.99)
& (hgdp1kg_tobwgs_joined.IB.f_stat > -0.25))
hgdp1kg_tobwgs_joined = hgdp1kg_tobwgs_joined.cache()
nrows = hgdp1kg_tobwgs_joined.count_rows()
print(f'hgdp1kg_tobwgs_joined.count_rows() = {nrows}')
hgdp1kg_tobwgs_joined = hgdp1kg_tobwgs_joined.sample_rows(
NUM_ROWS_BEFORE_LD_PRUNE / nrows, seed=12345)
pruned_variant_table = hl.ld_prune(hgdp1kg_tobwgs_joined.GT,
r2=0.1,
bp_window_size=500000)
hgdp1kg_tobwgs_joined = hgdp1kg_tobwgs_joined.filter_rows(
hl.is_defined(pruned_variant_table[hgdp1kg_tobwgs_joined.row_key]))
mt_path = f'{output}/tob_wgs_hgdp_1kg_filtered_variants.mt'
hgdp1kg_tobwgs_joined.write(mt_path)
def query(output): # pylint: disable=too-many-locals
"""Query script entry point."""
hl.init(default_reference='GRCh38')
hgdp_1kg = hl.read_matrix_table(GNOMAD_HGDP_1KG_MT)
tob_wgs = hl.read_matrix_table(TOB_WGS).key_rows_by('locus', 'alleles')
loadings = hl.read_table(GNOMAD_LIFTOVER_LOADINGS).key_by(
'locus', 'alleles')
# filter to loci that are contained in both tables and the loadings after densifying
tob_wgs = hl.experimental.densify(tob_wgs)
hgdp_1kg = hgdp_1kg.filter_rows(
hl.is_defined(loadings.index(hgdp_1kg['locus'], hgdp_1kg['alleles']))
& hl.is_defined(
tob_wgs.index_rows(hgdp_1kg['locus'], hgdp_1kg['alleles'])))
tob_wgs = tob_wgs.semi_join_rows(hgdp_1kg.rows())
# Entries and columns must be identical
tob_wgs_select = tob_wgs.select_entries(
GT=lgt_to_gt(tob_wgs.LGT, tob_wgs.LA))
hgdp_1kg_select = hgdp_1kg.select_entries(hgdp_1kg.GT)
hgdp_1kg_select = hgdp_1kg_select.select_cols()
# Join datasets
hgdp1kg_tobwgs_joined = hgdp_1kg_select.union_cols(tob_wgs_select)
# Add in metadata information
hgdp_1kg_metadata = hgdp_1kg.cols()
hgdp1kg_tobwgs_joined = hgdp1kg_tobwgs_joined.annotate_cols(
hgdp_1kg_metadata=hgdp_1kg_metadata[hgdp1kg_tobwgs_joined.s])
mt_path = f'{output}/hgdp1kg_tobwgs_joined_all_samples.mt'
if not hl.hadoop_exists(mt_path):
hgdp1kg_tobwgs_joined.write(mt_path)
hgdp1kg_tobwgs_joined = hl.read_matrix_table(mt_path)
# Perform PCA
eigenvalues_path = f'{output}/eigenvalues.csv'
scores_path = f'{output}/scores.ht'
loadings_path = f'{output}/loadings.ht'
eigenvalues, scores, loadings = hl.hwe_normalized_pca(
hgdp1kg_tobwgs_joined.GT, compute_loadings=True, k=20)
# save the list of eigenvalues
eigenvalues_df = pd.DataFrame(eigenvalues)
eigenvalues_df.to_csv(eigenvalues_path, index=False)
# save the scores and loadings as a hail table
scores.write(scores_path, overwrite=True)
loadings.write(loadings_path, overwrite=True)
def query(output, pop): # pylint: disable=too-many-locals
"""Query script entry point."""
hl.init(default_reference='GRCh38')
mt = hl.read_matrix_table(HGDP1KG_TOBWGS)
if pop:
# Get samples from the specified population only
mt = mt.filter_cols((
mt.hgdp_1kg_metadata.population_inference.pop == pop.lower())
| (mt.s.contains('TOB')))
else:
mt = mt.filter_cols(mt.s.contains('TOB'))
mt = mt.annotate_rows(af=hl.agg.mean(mt.GT.n_alt_alleles()) / 2)
loadings = hl.read_table(LOADINGS)
loadings = loadings.annotate(af=mt.rows()[loadings.key].af)
reprocessed_samples = hl.read_matrix_table(REPROCESSED_1KG)
reprocessed_samples = hl.experimental.densify(reprocessed_samples)
reprocessed_samples = reprocessed_samples.annotate_entries(
GT=lgt_to_gt(reprocessed_samples.LGT, reprocessed_samples.LA))
ht = pc_project(reprocessed_samples.GT, loadings.loadings, loadings.af)
ht = ht.key_by(s=ht.s + '_reprocessed')
pcs = hl.read_table(SCORES)
union_scores = ht.union(pcs)
union_scores = union_scores.annotate(
original=(union_scores.s == 'HG02238')
| (union_scores.s == 'NA12248')
| (union_scores.s == 'NA20502'),
reprocessed=union_scores.s.contains('reprocessed'),
)
expr = (
hl.case().when(
(union_scores.original)
& (
union_scores.reprocessed # pylint: disable=singleton-comparison
== False # noqa: E712
),
'original',
).when(
(union_scores.original == False) # pylint: disable=singleton-comparison
& (union_scores.reprocessed),
'reprocessed',
).default('unedited'))
union_scores = union_scores.annotate(cohort_sample_codes=expr)
# get percentage of variance explained
eigenvalues = hl.import_table(EIGENVALUES)
eigenvalues = eigenvalues.to_pandas()
eigenvalues.columns = ['eigenvalue']
eigenvalues = pd.to_numeric(eigenvalues.eigenvalue)
variance = eigenvalues.divide(float(eigenvalues.sum())) * 100
variance = variance.round(2)
# plot
labels = union_scores.cohort_sample_codes
sample_names = union_scores.s
cohort_sample_codes = list(set(labels.collect()))
tooltips = [('labels', '@label'), ('samples', '@samples')]
for i in range(0, 10):
pc1 = i
pc2 = i + 1
plot_filename = (f'{output}/reprocessed_sample_projection_pc' +
str(i + 1) + '.png')
if not hl.hadoop_exists(plot_filename):
plot = figure(
title='Reprocessed Sample Projection',
x_axis_label='PC' + str(pc1 + 1) + ' (' + str(variance[pc1]) +
'%)',
y_axis_label='PC' + str(pc2 + 1) + ' (' + str(variance[pc1]) +
'%)',
tooltips=tooltips,
)
source = ColumnDataSource(
dict(
x=union_scores.scores[pc1].collect(),
y=union_scores.scores[pc2].collect(),
label=labels.collect(),
samples=sample_names.collect(),
))
plot.circle(
'x',
'y',
alpha=0.5,
source=source,
size=8,
color=factor_cmap('label', Dark2[len(cohort_sample_codes)],
cohort_sample_codes),
legend_group='label',
)
plot.add_layout(plot.legend[0], 'left')
with hl.hadoop_open(plot_filename, 'wb') as f:
get_screenshot_as_png(plot).save(f, format='PNG')
plot_filename_html = ('reprocessed_sample_projection_pc' +
str(i + 1) + '.html')
output_file(plot_filename_html)
save(plot)
subprocess.run(['gsutil', 'cp', plot_filename_html, output],
check=False)
def query(): # pylint: disable=too-many-locals
"""Query script entry point."""
hl.init(default_reference='GRCh38')
snp_chip = hl.read_matrix_table(SNP_CHIP)
tob_wgs = hl.read_matrix_table(TOB_WGS)
tob_wgs = hl.experimental.densify(tob_wgs)
tob_wgs = tob_wgs.annotate_entries(GT=lgt_to_gt(tob_wgs.LGT, tob_wgs.LA))
snp_chip = snp_chip.semi_join_rows(tob_wgs.rows())
snp_chip_path = output_path('snp_chip_filtered_by_tob_wgs.mt', 'tmp')
snp_chip = snp_chip.checkpoint(snp_chip_path)
# Perform PCA
eigenvalues, scores, loadings = hl.hwe_normalized_pca(
snp_chip.GT, compute_loadings=True, k=5)
scores_path = output_path('scores.ht', 'tmp')
loadings_path = output_path('loadings.ht', 'tmp')
scores = scores.checkpoint(scores_path)
loadings = loadings.checkpoint(loadings_path)
# make tob_wgs rows equivalent to the snp_chip rows
tob_wgs = tob_wgs.semi_join_rows(snp_chip.rows())
tob_wgs_path = output_path('tob_wgs_filtered_by_snp_chip.mt', 'tmp')
tob_wgs = tob_wgs.checkpoint(tob_wgs_path)
snp_chip = snp_chip.annotate_rows(
af=hl.agg.mean(snp_chip.GT.n_alt_alleles()) / 2)
loadings = loadings.annotate(af=snp_chip.rows()[loadings.key].af)
# project WGS samples onto PCA
ht = pc_project(tob_wgs.GT, loadings.loadings, loadings.af)
ht_path = output_path('pc_project_tob_wgs.ht', 'tmp')
ht = ht.checkpoint(ht_path)
scores = scores.key_by(s=scores.s + '_snp_chip')
union_scores = ht.union(scores)
variance = [(x / sum(eigenvalues) * 100) for x in eigenvalues]
variance = [round(x, 2) for x in variance]
# Get partner sample information
sample_names = union_scores.s.collect()
def sample_type(sample_name):
if sample_name.endswith('snp_chip'):
partner_name = re.sub('_snp_chip', '', sample_name)
tech = 'snp'
else:
partner_name = sample_name + '_snp_chip'
tech = 'wgs'
if partner_name in sample_names:
prefix = 'dual_'
else:
prefix = ''
return prefix + tech
# plot
labels = list(map(sample_type, sample_names))
cohort_sample_codes = list(set(labels))
tooltips = [('labels', '@label'), ('samples', '@samples')]
# Get number of PCs
number_of_pcs = len(eigenvalues)
for i in range(0, (number_of_pcs - 1)):
pc1 = i
pc2 = i + 1
plot = figure(
title='TOB-WGS + TOB SNP Chip',
x_axis_label=f'PC{pc1 + 1} ({variance[pc1]})%)',
y_axis_label=f'PC{pc2 + 1} ({variance[pc2]}%)',
tooltips=tooltips,
)
source = ColumnDataSource(
dict(
x=union_scores.scores[pc1].collect(),
y=union_scores.scores[pc2].collect(),
label=labels,
samples=sample_names,
))
source_filename = output_path(f'source_{pc2}.ht', 'tmp')
hl.Table.from_pandas(pd.DataFrame(source.data)).export(source_filename)
plot.circle(
'x',
'y',
alpha=0.5,
source=source,
size=8,
color=factor_cmap('label', Dark2[len(cohort_sample_codes)],
cohort_sample_codes),
legend_group='label',
)
plot.add_layout(plot.legend[0], 'left')
plot_filename = output_path(f'pc{pc2}.png', 'web')
with hl.hadoop_open(plot_filename, 'wb') as f:
get_screenshot_as_png(plot).save(f, format='PNG')
html = file_html(plot, CDN, 'my plot')
plot_filename_html = output_path(f'pc{pc2}.html', 'web')
with hl.hadoop_open(plot_filename_html, 'w') as f:
f.write(html)
