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Merged
FerriolCalvet merged 3 commits into from
Feb 13, 2025
Merged

Add updated filters to dev #249

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92ba2b0
update filters and add missing params to schema
FerriolCalvet Feb 5, 2025
d47f4de
upd implementation of cohort_n_rich
FerriolCalvet Feb 5, 2025
3934583
apply not_covered filter to somatic mutations
FerriolCalvet Feb 7, 2025
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49 changes: 9 additions & 40 deletions bin/filter_cohort.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,59 +7,26 @@

maf_df_file = sys.argv[1]
samp_name = sys.argv[2]
repetitive_variant_treshold = int(sys.argv[3])
repetitive_variant_threshold = int(sys.argv[3])
somatic_vaf_boundary = float(sys.argv[4])
n_rich_cohort_proportion = float(sys.argv[5])

maf_df = pd.read_csv(maf_df_file, compression='gzip', header = 0, sep='\t', na_values = custom_na_values)

sequenced_genes = list(pd.unique(maf_df["SYMBOL"]))



# def correct_vaf(maf):

# """
# Computes VAF_CORRECTED for the subset of variants satisfying 0 < VAF < 0.2
# Returns the input MAF with two new columns:
# VAF_CORRECTED with new corrected VAF else it copies the VAF
# IS_VAF_CORRECTED with a boolean that indicates whether the VAF has been corrected
# """

# # TODO revise the 0.2 VAF threshold to see if it can be kept across datasets
# df = maf[(0 < maf['VAF']) & (maf['VAF'] < 0.2)][['SAMPLE_ID', 'MUT_ID', 'VAF', 'DEPTH']]
# df = df.sort_values('DEPTH')
# N = df.shape[0]
# df['VAF_ROLLING_MEAN'] = df['VAF'].rolling(N // 25).mean()
# df['VAF_ROLLING_STD'] = df['VAF'].rolling(N // 25).std()
# stable_mean = df['VAF_ROLLING_MEAN'].values[-1]
# stable_std = df['VAF_ROLLING_STD'].values[-1]
# df['VAF_CORRECTED'] = df.apply(lambda r: (r['VAF'] - r['VAF_ROLLING_MEAN']) * (stable_std / r['VAF_ROLLING_STD']) + stable_mean, axis=1)
# df = maf.merge(df[['VAF_CORRECTED', 'MUT_ID', 'SAMPLE_ID']],
# on=['MUT_ID', 'SAMPLE_ID'],
# how='outer')
# df['IS_VAF_CORRECTED'] = ~df['VAF_CORRECTED'].isnull()
# df.loc[~df['IS_VAF_CORRECTED'], 'VAF_CORRECTED'] = df[~df['IS_VAF_CORRECTED']]['VAF'].values
# return df



# #######
# ### Add a corrected VAF column
# #######
# maf_df = correct_vaf(maf_df)
# print("VAF corrected")




#######
### Filter repetitive variants
#######

# TODO revise these numbers, the repetitive_variant_treshold is the boundary at which we start considering a mutation as "repetitive"
# TODO revise these numbers, the repetitive_variant_threshold is the boundary at which we start considering a mutation as "repetitive"
max_samples = len(pd.unique(maf_df["SAMPLE_ID"]))

n_samples = list(range(repetitive_variant_treshold, max_samples + 1))
n_samples = list(range(repetitive_variant_threshold, max_samples + 1))
if len(n_samples) == 0:
print("Not enough samples to identify potential repetitive variants!")

Expand All @@ -73,7 +40,7 @@
maf_df_f_somatic["count"] = 1
maf_df_f_somatic_pivot = maf_df_f_somatic.groupby("MUT_ID")["count"].sum().reset_index()

repetitive_variants = maf_df_f_somatic_pivot[maf_df_f_somatic_pivot["count"] >= repetitive_variant_treshold]["MUT_ID"]
repetitive_variants = maf_df_f_somatic_pivot[maf_df_f_somatic_pivot["count"] >= repetitive_variant_threshold]["MUT_ID"]

maf_df["repetitive_variant"] = maf_df["MUT_ID"].isin(repetitive_variants)

Expand All @@ -95,6 +62,8 @@
print("Not enough samples to identify cohort_n_rich mutations!")

else:
number_of_samples = max(2, (max_samples * n_rich_cohort_proportion) // 1)
print(f"flagging mutations that are n_rich in at least: {number_of_samples} samples as cohort_n_rich")

# work with already filtered df + somatic only to explore potential artifacts
# take only variant and sample info from the df
Expand All @@ -105,7 +74,7 @@
].agg({'SAMPLE_ID' : len, 'VAF_Ns' : min})
n_rich_vars_df = n_rich_vars_df.rename({'SAMPLE_ID' : 'frequency', 'VAF_Ns' : 'VAF_Ns_threshold'}, axis = 'columns')

n_rich_vars = list(n_rich_vars_df[n_rich_vars_df['frequency'] >= 2].index)
n_rich_vars = list(n_rich_vars_df[n_rich_vars_df['frequency'] >= number_of_samples].index)

maf_df["cohort_n_rich"] = maf_df["MUT_ID"].isin(n_rich_vars)

Expand All @@ -132,7 +101,7 @@
maf_df['frequency'] = maf_df['frequency'].fillna(0)
maf_df['VAF_Ns_threshold'] = maf_df['VAF_Ns_threshold'].fillna(1.1)

maf_df["cohort_n_rich_threshold"] = maf_df["VAF_Ns"] > maf_df['VAF_Ns_threshold']
maf_df["cohort_n_rich_threshold"] = maf_df["VAF_Ns"] >= maf_df['VAF_Ns_threshold']

maf_df["FILTER"] = maf_df[["FILTER","cohort_n_rich_threshold"]].apply(lambda x: add_filter(x["FILTER"], x["cohort_n_rich_threshold"], "cohort_n_rich_threshold"),
axis = 1
Expand Down
4 changes: 2 additions & 2 deletions conf/modules.config
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Original file line number Diff line number Diff line change
Expand Up @@ -316,7 +316,7 @@ process {
['"VAF_AM" : "le ', "${params.germline_threshold}", '"'].join('').trim(),
['"vd_VAF" : "le ', "${params.germline_threshold}", '"'].join('').trim(),
['"DEPTH" : "ge ', "${params.mutation_depth_threshold}", '"'].join('').trim(),
'"FILTER" : ["notcontains NM20", "notcontains n_rich", "notcontains cohort_n_rich_threshold", "notcontains no_pileup_support", "notcontains low_mappability" ]',
'"FILTER" : ["notcontains NM20", "notcontains p8", "notcontains n_rich", "notcontains cohort_n_rich_threshold", "notcontains cohort_n_rich", "notcontains no_pileup_support", "notcontains low_mappability", "notcontains not_covered" ]',
'"VAF_distorted_expanded_sq" : false',
].join(',\t').trim()
}
Expand All @@ -336,7 +336,7 @@ process {
ext.filters = { [['"VAF" : "le ', "${params.germline_threshold}", '"'].join('').trim(),
['"vd_VAF" : "le ', "${params.germline_threshold}", '"'].join('').trim(),
['"DEPTH" : "ge ', "${params.mutation_depth_threshold}", '"'].join('').trim(),
'"FILTER" : ["notcontains NM20", "notcontains n_rich", "notcontains cohort_n_rich_threshold", "notcontains no_pileup_support", "notcontains low_mappability" ]',
'"FILTER" : ["notcontains NM20", "notcontains p8", "notcontains n_rich", "notcontains cohort_n_rich_threshold", "notcontains cohort_n_rich", "notcontains no_pileup_support", "notcontains low_mappability", "notcontains not_covered" ]',
'"VAF_distorted_expanded_sq" : false',
].join(',\t').trim()
}
Expand Down
6 changes: 2 additions & 4 deletions modules/local/filtermaf/main.nf
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Original file line number Diff line number Diff line change
Expand Up @@ -20,12 +20,12 @@ process FILTER_BATCH {
task.ext.when == null || task.ext.when

script:
def args = task.ext.args ?: ""
def prefix = task.ext.prefix ?: "${meta.id}"
def repetitive_variant = task.ext.repetitive_variant ?: "${params.repetitive_variant_thres}"
def germline_threshold = task.ext.germline_threshold ?: "${params.germline_threshold}"
def proportion_samples_nrich = task.ext.prop_samples_nrich ?: "${params.prop_samples_nrich}"
"""
filter_cohort.py ${maf} ${prefix} ${repetitive_variant} ${germline_threshold}
filter_cohort.py ${maf} ${prefix} ${repetitive_variant} ${germline_threshold} ${proportion_samples_nrich}

cat <<-END_VERSIONS > versions.yml
"${task.process}":
Expand All @@ -34,8 +34,6 @@ process FILTER_BATCH {
"""

stub:
def args = task.ext.args ?: ''
def prefix = task.ext.prefix ?: "${meta.id}"
"""
touch all_samples.cohort.filtered.tsv.gz

Expand Down
4 changes: 4 additions & 0 deletions nextflow.config
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,9 @@ params {
custom_groups_file = null
custom_groups_separator = 'tab'

customize_annotation = false
custom_annotation_tsv = ''

use_custom_bedfile = false
custom_bedfile = null
use_custom_minimum_depth = 0
Expand Down Expand Up @@ -76,6 +79,7 @@ params {
mutation_depth_threshold = 40

repetitive_variant_thres = 5
prop_samples_nrich = 0.1

cosmic_ref_signatures = "COSMIC_v3.4_SBS_GRCh38.txt"
wgs_trinuc_counts = "assets/trinucleotide_counts/trinuc_counts.homo_sapiens.tsv"
Expand Down
38 changes: 38 additions & 0 deletions nextflow_schema.json
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Original file line number Diff line number Diff line change
Expand Up @@ -188,6 +188,30 @@
}
}
},
"custom_annotation_configs": {
"title": "Custom annotation configurations",
"type": "object",
"fa_icon": "fas fa-dna",
"description": "BED-like file to customize the annotation of variants falling in certain regions.",
"properties": {
"customize_annotation": {
"type": "boolean",
"description": "Do you want to use a custom BED file?",
"fa_icon": "fas fa-book",
"help_text": "Do you want to use a custom BED file?"
},
"custom_annotation_tsv": {
"type": "string",
"format": "file-path",
"exists": true,
"mimetype": "text/plain",
"default": null,
"description": "TSV file with the following columns: chromosome start end gene_name impactful_mutations neutral_impact new_impact",
"help_text": "TSV file with the following columns: chromosome start end gene_name impactful_mutations neutral_impact new_impact",
"fa_icon": "far fa-file-code"
}
}
},
"hotspot_bedfile_configs": {
"title": "Definition of hotspot regions configuration",
"type": "object",
Expand Down Expand Up @@ -425,6 +449,13 @@
"default": 5,
"fa_icon": "fas fa-book",
"help_text": ""
},
"prop_samples_nrich": {
"type": "number",
"description": "Any variant that is n_rich in this proportion of samples is flagged as cohort_n_rich variant.",
"default": 0.1,
"fa_icon": "fas fa-book",
"help_text": ""
}
}
},
Expand Down Expand Up @@ -545,6 +576,13 @@
"help_text": "",
"default": "--no_stats --cache --offline --symbol --protein --canonical --af_gnomadg --af_gnomade"
},
"vep_params_panel": {
"type": "string",
"description": "Additional parameters for running Ensembl VEP for the panel annotation.",
"fa_icon": "fas fa-book",
"help_text": "",
"default": "--no_stats --cache --offline --symbol --protein --canonical"
},
"download_cache": {
"type": "boolean",
"description": "Do you want to download the cache? Activate it only if necessary.",
Expand Down