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Merged
FerriolCalvet merged 7 commits into from
Mar 20, 2025
Merged

260 specific consequence type filters for tert promoter in proportion of mutated epithelium #261

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8b77262
update mutated genomes from VAF module
koszulordie Mar 18, 2025
e6e5da1
update mutgenomes_driver_priority.py to allow csqn type recoding
koszulordie Mar 18, 2025
e9297b3
add nextflow handling of recoding genes
FerriolCalvet Mar 19, 2025
e6eec49
fix missing param definition in modules.config
FerriolCalvet Mar 19, 2025
1467e49
update driver priority code
FerriolCalvet Mar 20, 2025
b64c750
fix missing update
FerriolCalvet Mar 20, 2025
ddaa087
update filtering in modules.config
FerriolCalvet Mar 20, 2025
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117 changes: 48 additions & 69 deletions bin/mutgenomes_driver_priority.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
from utils import inclusion_exclusion
from utils_impacts import broadimpact_grouping_dict


def compute_prior(alt_read_count, depth):

mean_p = (1 + alt_read_count.sum()) / depth.sum()
Expand Down Expand Up @@ -72,13 +73,25 @@ def compute_covered_genomes(alt_read_count, depth):
return res


def snv_am(sample, somatic_mutations_file, omega_file, chosen_impacts=['missense', 'truncating']):
def recode_csqn_type(df, gene, new_csqn_type):

df['Consequence_broader'] = df.apply(lambda s: \
new_csqn_type if s['GENE'] == gene \
else s['Consequence_broader'],
axis=1)
return df
def snv_am(sample, somatic_mutations_file, omega_file, chosen_impacts=['missense', 'truncating'], recoded_genes=[]):

# parse mutations
somatic_mutations = pd.read_csv(somatic_mutations_file, sep='\t', low_memory=False)
consequences_of_interest = [ broadimpact_grouping_dict[csqn] for csqn in chosen_impacts]
consequences_of_interest = set([item for sublist in consequences_of_interest for item in sublist])

# recode csqn type
for gene in recoded_genes:
somatic_mutations = recode_csqn_type(somatic_mutations, gene, 'missense')
print(gene, somatic_mutations[somatic_mutations['GENE'] == gene]['Consequence_broader'].unique())

mutations = somatic_mutations[(somatic_mutations['TYPE'] == 'SNV')
& (somatic_mutations['Consequence_broader'].isin(consequences_of_interest))
].reset_index(drop = True)
Expand All @@ -103,47 +116,26 @@ def snv_am(sample, somatic_mutations_file, omega_file, chosen_impacts=['missense

for csqn in chosen_impacts:

# total
df_dict = {}

df_all = mutations[(mutations['GENE'] == gene) & (mutations['Consequence_broader'].isin(broadimpact_grouping_dict[csqn]))]
df_all = mutations[(mutations['GENE'] == gene) & (mutations['Consequence_broader'] == csqn)]
df_all = df_all.sort_values(by=['VAF_AM'], ascending=False)
df_all.reset_index(drop=True, inplace=True)
df_dict['TOTAL'] = df_all

# upper

ind = int(excess_dict[gene][csqn][2] * df_all.shape[0])
try:
assert(ind >= 1)
df_upper = df_all.loc[:ind - 1].copy()
except:
df_upper = pd.DataFrame(columns=df_all.columns)

# mean

ind = int(excess_dict[gene][csqn][1] * df_all.shape[0])
try:
assert(ind >= 1)
df_mean = df_all.loc[:ind - 1].copy()
except:
df_mean = pd.DataFrame(columns=df_all.columns)

# lower

ind = int(excess_dict[gene][csqn][0] * df_all.shape[0])
try:
assert(ind >= 1)
df_lower = df_all.loc[:ind - 1].copy()
except:
df_lower = pd.DataFrame(columns=df_all.columns)
for i, bound in enumerate(['LOWER', 'MEAN', 'UPPER']):
ind = int(excess_dict[gene][csqn][i] * df_all.shape[0])
try:
assert(ind >= 1)
dg = df_all.loc[:ind - 1].copy()
except:
dg = pd.DataFrame(columns=df_all.columns)
df_dict[bound] = dg

res_dict['gene'] = res_dict['gene'] + [gene]
res_dict['impact'] = res_dict['impact'] + [csqn]
res_dict['chr'] = res_dict['chr'] + [gene_chr_dict[gene]]

# summarize in a dictionary

df_dict = {'TOTAL': df_all, 'UPPER': df_upper, 'MEAN': df_mean, 'LOWER': df_lower}

for suffix in ['TOTAL', 'UPPER', 'MEAN', 'LOWER']:

if df_dict[suffix].shape[0] == 0:
Expand Down Expand Up @@ -209,18 +201,23 @@ def indel_am(sample, somatic_mutations_file):
return df[['SAMPLE'] + all_columnss]


def snv_nd(sample, somatic_mutations_file, omega_file, chosen_impacts=['missense', 'truncating']):
def snv_nd(sample, somatic_mutations_file, omega_file, chosen_impacts=['missense', 'truncating'], recoded_genes=[]):

# parse mutations
somatic_mutations = pd.read_csv(somatic_mutations_file, sep='\t', low_memory=False)
consequences_of_interest = [ broadimpact_grouping_dict[csqn] for csqn in chosen_impacts]
consequences_of_interest = set([item for sublist in consequences_of_interest for item in sublist])

# recode csqn type
for gene in recoded_genes:
somatic_mutations = recode_csqn_type(somatic_mutations, gene, 'missense')
print(gene, somatic_mutations[somatic_mutations['GENE'] == gene]['Consequence_broader'].unique())

mutations = somatic_mutations[
(somatic_mutations['TYPE'] == 'SNV')
& (somatic_mutations['Consequence_broader'].isin(consequences_of_interest))
].reset_index(drop = True)

gene_chr_dict = { x : y for x, y in somatic_mutations[['GENE', 'CHROM']].drop_duplicates().values }

mutations = mutations[mutations['ALT_DEPTH_ND'] > 0]
Expand All @@ -244,47 +241,26 @@ def snv_nd(sample, somatic_mutations_file, omega_file, chosen_impacts=['missense

for csqn in chosen_impacts:

# total
df_dict = {}

df_all = mutations[(mutations['GENE'] == gene) & (mutations['Consequence_broader'].isin(broadimpact_grouping_dict[csqn]))]
df_all = df_all.sort_values(by=['VAF_ND'], ascending=False)
df_all.reset_index(drop=True, inplace=True)
df_dict['TOTAL'] = df_all

# upper

ind = int(excess_dict[gene][csqn][2] * df_all.shape[0])
try:
assert(ind >= 1)
df_upper = df_all.loc[:ind - 1].copy()
except:
df_upper = pd.DataFrame(columns=df_all.columns)

# mean

ind = int(excess_dict[gene][csqn][1] * df_all.shape[0])
try:
assert(ind >= 1)
df_mean = df_all.loc[:ind - 1].copy()
except:
df_mean = pd.DataFrame(columns=df_all.columns)

# lower

ind = int(excess_dict[gene][csqn][0] * df_all.shape[0])
try:
assert(ind >= 1)
df_lower = df_all.loc[:ind - 1].copy()
except:
df_lower = pd.DataFrame(columns=df_all.columns)
for i, bound in enumerate(['LOWER', 'MEAN', 'UPPER']):
ind = int(excess_dict[gene][csqn][i] * df_all.shape[0])
try:
assert(ind >= 1)
dg = df_all.loc[:ind - 1].copy()
except:
dg = pd.DataFrame(columns=df_all.columns)
df_dict[bound] = dg

res_dict['gene'] = res_dict['gene'] + [gene]
res_dict['impact'] = res_dict['impact'] + [csqn]
res_dict['chr'] = res_dict['chr'] + [gene_chr_dict[gene]]

# summarize in a dictionary

df_dict = {'TOTAL': df_all, 'UPPER': df_upper, 'MEAN': df_mean, 'LOWER': df_lower}

for suffix in ['TOTAL', 'UPPER', 'MEAN', 'LOWER']:

if df_dict[suffix].shape[0] == 0:
Expand All @@ -308,7 +284,8 @@ def snv_nd(sample, somatic_mutations_file, omega_file, chosen_impacts=['missense
@click.option('--sample')
@click.option('--somatic-mutations-file')
@click.option('--omega-file')
def run_all(sample, somatic_mutations_file, omega_file):
@click.option('--recoded-genes', default='', type=str)
def run_all(sample, somatic_mutations_file, omega_file, recoded_genes):
"""
intent:
script that combines the 3 covered epithelium functions,
Expand All @@ -317,10 +294,12 @@ def run_all(sample, somatic_mutations_file, omega_file):
nextflow module

"""
# recodification of csqn type is only applied to SNVs
recoded_genes = recoded_genes.split(',')

df_snv_am = snv_am(sample, somatic_mutations_file, omega_file)
df_snv_am = snv_am(sample, somatic_mutations_file, omega_file, recoded_genes= recoded_genes)
df_indel_am = indel_am(sample, somatic_mutations_file)
df_snv_nd = snv_nd(sample, somatic_mutations_file, omega_file)
df_snv_nd = snv_nd(sample, somatic_mutations_file, omega_file, recoded_genes= recoded_genes)

all_dataframes = [df_snv_am, df_indel_am, df_snv_nd]
df_merged = reduce(lambda left, right: pd.merge(left, right, on=['chr', 'gene', 'impact', 'SAMPLE'], how='outer'), all_dataframes)
Expand Down
6 changes: 3 additions & 3 deletions bin/mutgenomes_summary_tables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,7 @@ def gather(metadata_file):

for label in ['GENOMES_SNV_AM', 'GENOMES_SNV_ND']:
for k in ['LOWER', 'MEAN', 'UPPER', 'TOTAL']:

df_all_annotated[f'CELLS_DOUBLE_HIT_{'_'.join(label.split('_')[1:])}_{k}'] = df_all_annotated[f'{label}_{k}'].values

df_all_annotated[f'CELLS_SINGLE_HIT_{'_'.join(label.split('_')[1:])}_{k}'] = df_all_annotated.apply(
Expand Down Expand Up @@ -80,13 +80,13 @@ def gather(metadata_file):

for label in ['GENOMES_SNV_AM', 'GENOMES_SNV_ND']:
for k in ['LOWER', 'MEAN', 'UPPER', 'TOTAL']:

df_gene_annotated[f'CELLS_DOUBLE_HIT_{'_'.join(label.split('_')[1:])}_{k}'] = df_gene_annotated[f'{label}_{k}'].values

df_gene_annotated[f'CELLS_SINGLE_HIT_{'_'.join(label.split('_')[1:])}_{k}'] = df_gene_annotated.apply(
lambda r: r[f'{label}_{k}'] if ((r['chr'] == 'chrX') and (r['SEX'] == 'M')) else 2 * r[f'{label}_{k}'],
axis=1)

# indels

label = 'GENOMES_INDEL_AM'
Expand Down
5 changes: 3 additions & 2 deletions conf/modules.config
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Original file line number Diff line number Diff line change
Expand Up @@ -486,17 +486,18 @@ process {
if (params.mutated_cells_vaf) {
if (params.all_duplex_counts){
withName: 'SUBSETMUTEPIVAFAM' {
ext.filters = { ['"canonical_Protein_affecting": "protein_affecting"'].join(',\t').trim() }
ext.output_fmt = { ['"header": true',
'"columns": ["CHROM", "canonical_SYMBOL", "canonical_Consequence_broader", "VAF_AM", "ALT_DEPTH_AM", "DEPTH_AM", "canonical_Protein_affecting", "TYPE", "VAF_ND", "ALT_DEPTH_ND", "DEPTH_ND", "VAF", "ALT_DEPTH", "DEPTH", "MUT_ID"]',
'"colnames": ["CHROM", "GENE", "Consequence_broader", "VAF_AM", "ALT_DEPTH_AM", "DEPTH_AM", "Protein_affecting", "TYPE", "VAF_ND", "ALT_DEPTH_ND", "DEPTH_ND", "VAF", "ALT_DEPTH", "DEPTH", "MUT_ID"]'
].join(',\t').trim()
}

publishDir = [
enabled : false
]
}
withName: 'MUTATEDGENOMESFROMVAFAM' {
ext.recode_list = "${params.mutepi_genes_to_recode}"// "TERTpromoter"
}
}

}
Expand Down
4 changes: 3 additions & 1 deletion modules/local/mutated_genomes_from_vaf/main.nf
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Original file line number Diff line number Diff line change
Expand Up @@ -17,11 +17,13 @@ process MUTATED_GENOMES_FROM_VAF {

script:
def prefix = task.ext.prefix ?: "${meta.id}"
def recode = task.ext.recode_list ? "--recoded-genes ${task.ext.recode_list}" : ""
"""
mutgenomes_driver_priority.py \\
--sample ${prefix} \\
--somatic-mutations-file ${mutations} \\
--omega-file ${omegas} ;
--omega-file ${omegas} \\
${recode} ;

cat <<-END_VERSIONS > versions.yml
"${task.process}":
Expand Down
1 change: 1 addition & 0 deletions nextflow.config
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Original file line number Diff line number Diff line change
Expand Up @@ -53,6 +53,7 @@ params {
signatures = false
mutationrate = false
mutated_cells_vaf = false
mutepi_genes_to_recode = null
expected_mutated_cells = false
dnds = false

Expand Down
7 changes: 6 additions & 1 deletion nextflow_schema.json
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Expand Up @@ -319,9 +319,14 @@
},
"mutated_cells_vaf": {
"type": "boolean",
"description": "Do you want to run mutated_cells_reads using the VAFs?",
"description": "Do you want to compute the proportion of mutated cells using the VAFs?",
"fa_icon": "fas fa-book"
},
"mutepi_genes_to_recode": {
"type": "string",
"description": "Do you want to consider all mutations in a gene for the mutated epithelium from VAF?",
"fa_icon": "fas fa-book"
},
"expected_mutated_cells": {
"type": "boolean",
"description": "Do you want to run expected_mutated_cells?",
Expand Down