116 lines
4.0 KiB
Python
116 lines
4.0 KiB
Python
import pandas as pd
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from mutations import CategorizedMutations
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from variants import CategorizedVariantRecords
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import utils
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from collections import defaultdict
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def mutation_site_count_vs_sample_type(
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cat_muts: CategorizedMutations,
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cat_vars: CategorizedVariantRecords,
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viral_strand,
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region,
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non_synonymous,
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allow_fishers,
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):
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# Create list of earliest mutations
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earliest_mutations_category = "earliest mutations"
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cat_muts.pcintersect_category_groups_with_categories(
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earliest_mutations_category,
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"sample type",
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tuple(cat_muts.flattened_pcategory_group("patient earliest"))
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)
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working_category_name = earliest_mutations_category
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next_working_category_name = earliest_mutations_category
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if viral_strand != "all":
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next_working_category_name += f" - {viral_strand}"
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cat_muts.pcintersect_category_groups_with_categories(
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next_working_category_name,
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working_category_name,
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tuple(cat_muts.pget_category_group("viral lineage")[viral_strand]),
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)
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working_category_name = next_working_category_name
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if region != "all":
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next_working_category_name += f" - {region}"
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cat_muts.pcintersect_category_groups_with_categories(
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next_working_category_name,
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working_category_name,
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tuple(cat_muts.pget_category_group("regions")[region]),
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)
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working_category_name = next_working_category_name
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if non_synonymous:
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next_working_category_name += " - non-synonymous"
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cat_muts.pcintersect_category_groups_with_categories(
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next_working_category_name,
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working_category_name,
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tuple(cat_muts.pget_category("non-synonymous")),
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)
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working_category_name = next_working_category_name
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mutated_site_counts_per_sample_type = (
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cat_muts.mutation_sites_for_pccategory_group(working_category_name)
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)
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reference_site_counts_per_sample_type = dict(mutated_site_counts_per_sample_type)
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for key, value in reference_site_counts_per_sample_type.items():
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reference_site_counts_per_sample_type[key] = len(cat_muts.ref_sequence) - value
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conti_table_dict = {
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"Mutated": mutated_site_counts_per_sample_type,
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"Reference": reference_site_counts_per_sample_type,
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}
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contigency_test = pd.DataFrame(conti_table_dict)
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result = utils.row_pairwise_dependency_test(contigency_test, allow_fishers)
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return (
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"Is mutation site count dependent on sample type?",
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"Here, we will count the sites of mutation in the "
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"reference sequence and compare that number for each "
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"type of mutation. The following is the table we will "
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"we will use: ",
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utils.publish_results(result),
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)
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# TODO Write individual nucleotide test
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def individual_mutation_count_vs_sample_type(
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cat_muts: CategorizedMutations,
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cat_vars: CategorizedVariantRecords,
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viral_strand,
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region,
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non_synonymous,
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allow_fishers,
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):
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results = []
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for location, location_based_mutations in cat_muts.pget_category_group("locations"):
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contingency_table_per_location = defaultdict(dict)
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for sample_type, sample_type_based_mutations in cat_muts.pget_category_group(
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"sample type"
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):
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contingency_table_per_location[f"Mutations at {location}"][
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sample_type
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] = len(
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cat_muts.intersect(
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location_based_mutations, sample_type_based_mutations
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)
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)
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contingency_table_per_location["Reference"][sample_type] = len(
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location_based_mutations
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) - len(cat_muts.pget_category("patients"))
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utils.publish_results(contingency_table_per_location, results)
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return "Location based mutations vs Sample type", results
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# TODO Write distribution difference test (?)
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tests = [
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(
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"Correlation between the number of mutation locations and sample types",
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mutation_site_count_vs_sample_type,
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)
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]
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