def compute_fisher_exact(tb: hl.Table,
n_cases_col: str,
n_control_col: str,
total_cases_col: str,
total_controls_col: str,
correct_total_counts: bool,
root_col_name: str,
extra_fields: dict) -> hl.Table:
"""
Perform two-sided Fisher Exact test. Add extra annotations (if any)
:param tb: Hail Table
:param n_cases_col: field name with number of (affected) cases
:param n_control_col: field name with number of (affected) control
:param total_cases_col: field name with total number of cases
:param total_controls_col: field name with total number of controls
:param correct_total_counts: should the total numbers (case/control) be corrected to avoid duplicated counting?
:param root_col_name: field to be annotated with test results
:param extra_fields: Extra filed (must be a dict) to be annotated
:return: Hail Table with Fisher Exact test results.
"""
# compute fisher exact
if correct_total_counts:
fet = hl.fisher_exact_test(c1=hl.int32(tb[n_cases_col]),
c2=hl.int32(tb[n_control_col]),
c3=hl.int32(tb[total_cases_col]) - hl.int32(tb[n_cases_col]),
c4=hl.int32(tb[total_controls_col]) - hl.int32(tb[n_control_col]))
else:
fet = hl.fisher_exact_test(c1=hl.int32(tb[n_cases_col]),
c2=hl.int32(tb[n_control_col]),
c3=hl.int32(tb[total_cases_col]),
c4=hl.int32(tb[total_controls_col]))
tb = (tb
.annotate(**{root_col_name: fet})
.flatten()
)
if len(extra_fields) == 0:
return tb
else:
return tb.annotate(**extra_fields)
def test(self):
schema = hl.tstruct(a=hl.tint32, b=hl.tint32, c=hl.tint32, d=hl.tint32, e=hl.tstr,
f=hl.tarray(hl.tint32),
g=hl.tarray(
hl.tstruct(x=hl.tint32, y=hl.tint32, z=hl.tstr)),
h=hl.tstruct(a=hl.tint32, b=hl.tint32, c=hl.tstr),
i=hl.tbool,
j=hl.tstruct(x=hl.tint32, y=hl.tint32, z=hl.tstr))
rows = [{'a': 4, 'b': 1, 'c': 3, 'd': 5,
'e': "hello", 'f': [1, 2, 3],
'g': [hl.Struct(x=1, y=5, z='banana')],
'h': hl.Struct(a=5, b=3, c='winter'),
'i': True,
'j': hl.Struct(x=3, y=2, z='summer')}]
kt = hl.Table.parallelize(rows, schema)
result = convert_struct_to_dict(kt.annotate(
chisq=hl.chisq(kt.a, kt.b, kt.c, kt.d),
ctt=hl.ctt(kt.a, kt.b, kt.c, kt.d, 5),
dict=hl.dict(hl.zip([kt.a, kt.b], [kt.c, kt.d])),
dpois=hl.dpois(4, kt.a),
drop=kt.h.drop('b', 'c'),
exp=hl.exp(kt.c),
fet=hl.fisher_exact_test(kt.a, kt.b, kt.c, kt.d),
hwe=hl.hardy_weinberg_p(1, 2, 1),
index=hl.index(kt.g, 'z'),
is_defined=hl.is_defined(kt.i),
is_missing=hl.is_missing(kt.i),
is_nan=hl.is_nan(hl.float64(kt.a)),
json=hl.json(kt.g),
log=hl.log(kt.a, kt.b),
log10=hl.log10(kt.c),
or_else=hl.or_else(kt.a, 5),
or_missing=hl.or_missing(kt.i, kt.j),
pchisqtail=hl.pchisqtail(kt.a, kt.b),
pcoin=hl.rand_bool(0.5),
pnorm=hl.pnorm(0.2),
pow=2.0 ** kt.b,
ppois=hl.ppois(kt.a, kt.b),
qchisqtail=hl.qchisqtail(kt.a, kt.b),
range=hl.range(0, 5, kt.b),
rnorm=hl.rand_norm(0.0, kt.b),
rpois=hl.rand_pois(kt.a),
runif=hl.rand_unif(kt.b, kt.a),
select=kt.h.select('c', 'b'),
sqrt=hl.sqrt(kt.a),
to_str=[hl.str(5), hl.str(kt.a), hl.str(kt.g)],
where=hl.cond(kt.i, 5, 10)
).take(1)[0])
def fet_expr(het_count_exp: hl.expr.Int64Expression,
hom_count_expr: hl.expr.Int64Expression):
return hl.bind(
lambda x: hl.struct(
counts=x,
dominant=hl.fisher_exact_test(x[0][0], x[0][1] + x[0][2],
x[1][0], x[1][1] + x[1][2]),
recessive=hl.fisher_exact_test(x[0][0] + x[0][1], x[0][
2], x[1][0] + x[1][1], x[1][2])),
hl.bind(
lambda x: [
[
hl.int32(
hl.cond(x.contains(False), x[False].get(0, 0),
0)),
hl.int32(
hl.cond(x.contains(False), x[False].get(1, 0),
0)),
hl.int32(
hl.cond(x.contains(False), x[False].get(2, 0),
0))
],
[
hl.int32(
hl.cond(x.contains(True), x[True].get(0, 0), 0)
),
hl.int32(
hl.cond(x.contains(True), x[True].get(1, 0), 0)
),
hl.int32(
hl.cond(x.contains(True), x[True].get(2, 0), 0)
)
],
],
hl.agg.group_by(
mt.is_case,
hl.agg.counter(
hl.min(2, het_count_exp + 2 * hom_count_expr)))))
def fs_from_sb(
sb: Union[hl.expr.ArrayNumericExpression, hl.expr.ArrayExpression],
normalize: bool = True,
min_cell_count: int = 200,
min_count: int = 4,
min_p_value: float = 1e-320,
) -> hl.expr.Int64Expression:
"""
Computes `FS` (Fisher strand balance) annotation from the `SB` (strand balance table) field.
`FS` is the phred-scaled value of the double-sided Fisher exact test on strand balance.
Using default values will have the same behavior as the GATK implementation, that is:
- If sum(counts) > 2*`min_cell_count` (default to GATK value of 200), they are normalized
- If sum(counts) < `min_count` (default to GATK value of 4), returns missing
- Any p-value < `min_p_value` (default to GATK value of 1e-320) is truncated to that value
In addition to the default GATK behavior, setting `normalize` to `False` will perform a chi-squared test
for large counts (> `min_cell_count`) instead of normalizing the cell values.
.. note::
This function can either take
- an array of length four containing the forward and reverse strands' counts of ref and alt alleles: [ref fwd, ref rev, alt fwd, alt rev]
- a two dimensional array with arrays of length two, containing the counts: [[ref fwd, ref rev], [alt fwd, alt rev]]
GATK code here: https://github.com/broadinstitute/gatk/blob/master/src/main/java/org/broadinstitute/hellbender/tools/walkers/annotator/FisherStrand.java
:param sb: Count of ref/alt reads on each strand
:param normalize: Whether to normalize counts is sum(counts) > min_cell_count (normalize=True), or use a chi sq instead of FET (normalize=False)
:param min_cell_count: Maximum count for performing a FET
:param min_count: Minimum total count to output FS (otherwise null it output)
:return: FS value
"""
if not isinstance(sb, hl.expr.ArrayNumericExpression):
sb = hl.bind(lambda x: hl.flatten(x), sb)
sb_sum = hl.bind(lambda x: hl.sum(x), sb)
# Normalize table if counts get too large
if normalize:
fs_expr = hl.bind(
lambda sb, sb_sum: hl.cond(
sb_sum <= 2 * min_cell_count,
sb,
sb.map(lambda x: hl.int(x / (sb_sum / min_cell_count))),
),
sb,
sb_sum,
)
# FET
fs_expr = to_phred(
hl.max(
hl.fisher_exact_test(
fs_expr[0], fs_expr[1], fs_expr[2], fs_expr[3]
).p_value,
min_p_value,
)
)
else:
fs_expr = to_phred(
hl.max(
hl.contingency_table_test(
sb[0], sb[1], sb[2], sb[3], min_cell_count=min_cell_count
).p_value,
min_p_value,
)
)
# Return null if counts <= `min_count`
return hl.or_missing(
sb_sum > min_count, hl.max(0, fs_expr) # Needed to avoid -0.0 values
)
e_cases = filtered_e.aggregate(hl.agg.sum(filtered_e.nontopmed_cases))
e_homs = filtered_e.aggregate(hl.agg.max(filtered_e.nontopmed_hom))
e_controls = e_max_AN - e_cases - (e_homs * 2)
g_max_AN = filtered_g.aggregate(hl.agg.max(filtered_g.nontopmed_AN))
g_cases = filtered_g.aggregate(hl.agg.sum(filtered_g.nontopmed_cases))
g_homs = filtered_g.aggregate(hl.agg.max(filtered_g.nontopmed_hom))
g_controls = g_max_AN - g_cases - (g_homs * 2)
print("Exomes max AN: {}".format(e_max_AN), file=f)
print("Exomes cases: {}".format(e_cases), file=f)
print("Exome Homs: {}".format(e_homs), file=f)
print("Exome Controls: {}".format(e_controls), file=f)
print("Genomes max AN: {}".format(g_max_AN), file=f)
print("Genomes cases: {}".format(g_cases), file=f)
print("Genomes Homs: {}".format(g_homs), file=f)
print("Genome controls: {}".format(g_controls), file=f)
print("Running Fisher Exact Test")
tot_cases = int(e_cases + g_cases)
tot_controls = int(e_controls + g_controls)
print("Combined controls: {}".format(tot_controls), file=f)
print("Combined cases: {}".format(tot_cases), file=f)
result = hl.fisher_exact_test(3, 45, tot_cases, tot_controls)
print("Exporting Fisher Exact Test Results...")
result.export(
'/gpfs/ycga/project/kahle/sp2349/moyamoya/case_control/diaph1_cc.tsv')
print("Done!")
def gnomad_het_case_control(exp_cases,exp_controls,chrm='0',start=0,end=0,out_dir=os.getcwd(),name=""):
"""Performs case Control Test for specified coordinates using the Gnomad combined exome and genome dataset."""
#Convert chromosome integer to string
if type(chrm) == 'int':
chrm = str(chrm)
chrm = str(chrm)
#Check if name provided
if name == "":
name = "results_{}_{}-{}".format(chrm,start,end)
#Create folder structure for ouput
parent_dir = os.path.join(out_dir,"gnomad_case_control",name)
if os.path.exists(os.path.join(parent_dir)) == False:
os.makedirs(parent_dir)
out_f = os.path.join(parent_dir,"results_{}_{}-{}.out".format(chrm,start,end))
with open(out_f, "w") as f:
#Import gnomad combined exome and genome table
gnomad_e = hl.read_table('/gpfs/ycga/scratch60/kahle/sp2349/combined_weilai_mts/combined.filtered.final.gnomad.r2.1.1.sites.{}.mt'.format(chrm))
#Filter on Bravo coordinates
#filtered_e = gnomad_e.filter((gnomad_e.info.bravo <= 0.0005) | (gnomad_e.info.bravo.is_defined() == False))
#filtered_g = gnomad_g.filter((gnomad_g.info.bravo <= 0.0005) | (gnomad_g.info.bravo.is_defined() == False))
#Filter on Bravo coordinates
filtered_e = gnomad_e.filter(gnomad_e.bravo <= 0.0005)
#Filter on DIAPH1 coordinates
filtered_e = filtered_e.filter((filtered_e.locus >= hl.locus(chrm,start)) & (filtered_e.locus <= hl.locus(chrm,end)))
print("Exomes count filtered on gene: {}".format(filtered_e.count()),file=f)
#Filter for variants with AC == 0
filtered_e = filtered_e.filter(filtered_e.combined_nontopmed_AC > 0)
filtered_e
#Filter for LoF variants.If missense, CADD1.6 score >= 20 or MetaSVM == D
filtered_e = filtered_e.filter(((filtered_e["Exonic_refGene"] == 'nonsynonymous_SNV') & (filtered_e.CADD16snv_PHRED >=20)) |
((filtered_e["Exonic_refGene"] == 'nonsynonymous_SNV') & (filtered_e.MetaSVM_pred == "D")) |
(filtered_e["Func_refGene"] == 'splicing') |
(filtered_e["Exonic_refGene"] == 'frameshift_deletion') |
(filtered_e["Exonic_refGene"] == 'frameshift_insertion') |
(filtered_e["Exonic_refGene"] == 'stopgain') |
(filtered_e["Exonic_refGene"] == 'stoploss') |
(filtered_e["Func_refGene"] == 'exonic\\x3bsplicing'))
print("Lof var rows kept: {}".format(filtered_e.count()),file=f)
#Gather non_topmed indicies. Collect() results in a list containing a dictionary. Too see all groups use freq_index_dict.collect()[0].
#See Macarthur lab website for more information on the gnomad groups.
#group_e = filtered_e.freq_index_dict.collect()[0][group_sel]
#group_g = filtered_g.freq_index_dict.collect()[0][group_sel]
#print("Exome non_topmed index: {}".format(group_e),file=f)
#print("Genome non_topmed index: {}".format(group_g),file=f)
#Calculate Cases and Controls. Cases = AC - (2*homozygote_count). Controls = max(AN - cases)
filtered_e = filtered_e.annotate(nontopmed_cases=filtered_e.combined_nontopmed_AC-(filtered_e.combined_nontopmed_nhomalt *2))
filtered_e = filtered_e.annotate(nontopmed_AN=filtered_e.combined_nontopmed_AN)
filtered_e = filtered_e.annotate(nontopmed_hom=filtered_e.combined_nontopmed_nhomalt)
e_max_AN = filtered_e.aggregate(hl.agg.max(filtered_e.nontopmed_AN))
e_cases = filtered_e.aggregate(hl.agg.sum(filtered_e.nontopmed_cases))
e_homs = filtered_e.aggregate(hl.agg.sum(filtered_e.nontopmed_hom))
e_controls = e_max_AN-e_cases-(e_homs*2)
print("Combined max AN: {}".format(e_max_AN),file=f)
print("Combined cases: {}".format(e_cases),file=f)
print("Combined Homs: {}".format(e_homs),file=f)
print("Combined Controls: {}".format(e_controls),file=f)
print("Running Fisher Exact Test")
filtered_e.write(os.path.join(parent_dir,"results_{}_{}-{}.ht".format(chrm,start,end)),overwrite=True)
df = filtered_e.to_pandas()
df.to_csv(os.path.join(parent_dir,"results_{}_{}-{}.csv".format(chrm,start,end)))
result = hl.fisher_exact_test(exp_cases,exp_controls,e_cases,e_controls)
print("Exporting Fisher Exact Test Results...")
result.export(os.path.join(parent_dir,"results_{}_{}-{}.tsv".format(chrm,start,end)))
print("Done!")
(filtered.combined_nontopmed_nhomalt * 2))
#filtered = filtered.annotate(nontopmed_AN=filtered.combined_nontopmed_AN)
#filtered = filtered.annotate(nontopmed_hom=filtered.combined_nontopmed_nhomalt)
max_AN = filtered.aggregate(hl.agg.max(filtered.combined_nontopmed_AN))
cases = filtered.aggregate(hl.agg.sum(filtered.combined_nontopmed_cases))
homs = filtered.aggregate(hl.agg.sum(filtered.combined_nontopmed_nhomalt))
controls = max_AN - cases - (homs * 2)
print("Combined max AN: {}".format(max_AN), file=f)
print("Combined cases: {}".format(cases), file=f)
print("Combined Homs: {}".format(homs), file=f)
print("Combined Controls: {}".format(controls), file=f)
print("Running Fisher Exact Test")
filtered.write(
"/gpfs/ycga/project/kahle/sp2349/moyamoya/case_control/cc_output/diaph1_gnomad-combined_sam.ht",
overwrite=True)
df = filtered.to_pandas()
df.to_csv(
"/gpfs/ycga/project/kahle/sp2349/moyamoya/case_control/cc_output/diaph1_gnomad-combined_sam.csv"
)
result = hl.fisher_exact_test(3, 45, cases, controls)
print("Exporting Fisher Exact Test Results...")
result.export(
'/gpfs/ycga/project/kahle/sp2349/moyamoya/case_control/cc_output/diaph1_gnomad-combined_sam_cc.tsv'
)
print("Done!")
