#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import unittest
from types import SimpleNamespace
import singlecellmultiomics.bamProcessing.bamToCountTable
from singlecellmultiomics.bamProcessing.bamBinCounts import range_contains_overlap,blacklisted_binning
class TestIterables(unittest.TestCase):
def test_blacklisted_binning(self):
bin_size = 250
blacklist = [(450,1001),(1007,1019),(1550,1600),(2300,2510)]
blacklist = sorted(blacklist)
self.assertFalse(
range_contains_overlap( list( blacklisted_binning(0,2000,bin_size,blacklist) ) + blacklist)
)
class TestCountTable(unittest.TestCase):
def test_total_read_counting(self):
""" Test if the amount of raw reads in a bam file is counted properly """
df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table(
SimpleNamespace(
alignmentfiles=['./data/mini_nla_test.bam'],
head=None,
o=None,
bin=None,
binTag='DS',
sliding=None,
bedfile=None,
showtags=False,
featureTags=None,
joinedFeatureTags='reference_name',
byValue=None,
sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False,
minMQ=0,
filterXA=False,
dedup=False,
divideMultimapping=False,
doNotDivideFragments=True,
contig=None,
blacklist=None,
r1only=False,
r2only=False,
filterMP=False,
splitFeatures=False,
feature_delimiter=',',
noNames=False) , return_df=True)
# !samtools idxstats ./data/mini_nla_test.bam | head -n 1 | cut -f 3
self.assertEqual(df.loc['chr1'].sum(),563)
def test_total_read1_counting(self):
""" Test if the amount of valid deduped R1 reads in a bam file is counted properly
samtools view ./data/mini_nla_test.bam -f 64 -F 3840 | grep DS | wc -l : 210
"""
df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table(
SimpleNamespace(
alignmentfiles=['./data/mini_nla_test.bam'],
head=None,
o=None,
bin=None,
binTag='DS',
sliding=None,
bedfile=None,
showtags=False,
featureTags=None,
joinedFeatureTags='reference_name',
byValue=None,
sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False,
minMQ=0,
filterXA=False,
dedup=True,
divideMultimapping=False,
doNotDivideFragments=True,
contig=None,
blacklist=None,
r1only=True,
r2only=False,
filterMP=False,
splitFeatures=False,
feature_delimiter=',',
noNames=False) , return_df=True)
# !samtools idxstats ./data/mini_nla_test.bam | head -n 1 | cut -f 3
self.assertEqual(df.loc['chr1'].sum(),210)
def test_contig_selection(self):
""" Test if a contig is selected properly"""
df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table(
SimpleNamespace(
alignmentfiles=['./data/mini_nla_test.bam'],
head=None,
o=None,
bin=None,
binTag='DS',
sliding=None,
bedfile=None,
showtags=False,
featureTags=None,
joinedFeatureTags='reference_name',
byValue=None,
sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False,
contig='chr5',
minMQ=0,
filterXA=False,
dedup=False,
r1only=False,
r2only=False,
divideMultimapping=False,
doNotDivideFragments=True,
splitFeatures=False,
blacklist=None,
filterMP=False,
feature_delimiter=',',
noNames=False) , return_df=True)
# !samtools idxstats ./data/mini_nla_test.bam | head -n 1 | cut -f 3
self.assertEqual(df.sum().sum(),0)
def test_total_molecule_counting(self):
""" Test if the amount of molecules in a bam file is counted properly """
df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table(
SimpleNamespace(
alignmentfiles=['./data/mini_nla_test.bam'],
o=None,
head=None,
bin=None,
binTag='DS',
byValue=None,
sliding=None,
bedfile=None,
showtags=False,
featureTags=None,
joinedFeatureTags='reference_name',
sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False,
minMQ=0,
filterXA=False,
dedup=True,
divideMultimapping=False,
doNotDivideFragments=True,
contig=None,
r1only=False,
r2only=False,
blacklist=None,
filterMP=False,
splitFeatures=False,
feature_delimiter=',',
noNames=False) , return_df=True)
# !samtools view ./singlecellmultiomics/data/mini_nla_test.bam | grep 'RC:i:1' | wc -l
self.assertEqual(df.loc['chr1'].sum(),383)
def test_singleFeatureTags_molecule_counting(self):
""" Test if the single feature counting feature works """
df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table(
SimpleNamespace(
alignmentfiles=['./data/mini_nla_test.bam'],
o=None,
head=None,
bin=None,
sliding=None,
binTag=None,
byValue=None,
bedfile=None,
showtags=False,
featureTags='reference_name,RC',
joinedFeatureTags=None,
sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False,
minMQ=0,
filterXA=False,
dedup=False,
divideMultimapping=False,
contig=None,
r1only=False,
r2only=False,
keepOverBounds=False,
doNotDivideFragments=True,
blacklist=None,
filterMP=False,
splitFeatures=False,
feature_delimiter=',',
noNames=False) , return_df=True)
# !samtools view ./singlecellmultiomics/data/mini_nla_test.bam | grep 'RC:i:1' | wc -l
self.assertEqual(df.loc['chr1'].sum(),563)
self.assertEqual(df.loc['1'].sum(),383)
# Amount of RC:2 obs:
self.assertEqual(df.loc['2'].sum(),97)
def test_singleFeatureTags_molecule_counting_contig(self):
""" Test if the single feature counting feature works with -contig """
df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table(
SimpleNamespace(
alignmentfiles=['./data/mini_nla_test.bam'],
o=None,
head=None,
bin=None,
sliding=None,
binTag=None,
byValue=None,
bedfile=None,
showtags=False,
featureTags='reference_name,RC',
joinedFeatureTags=None,
sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False,
minMQ=0,
filterXA=False,
dedup=False,
divideMultimapping=False,
contig='chr1',
r1only=False,
r2only=False,
keepOverBounds=False,
doNotDivideFragments=True,
blacklist=None,
filterMP=False,
splitFeatures=False,
feature_delimiter=',',
noNames=False) , return_df=True)
# !samtools view ./singlecellmultiomics/data/mini_nla_test.bam | grep 'RC:i:1' | wc -l
self.assertEqual(df.loc['chr1'].sum(),563)
self.assertEqual(df.loc['1'].sum(),383)
# Amount of RC:2 obs:
self.assertEqual(df.loc['2'].sum(),97)
def test_bed_counting(self):
""" Test if the bed feature counting feature works """
df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table(
SimpleNamespace(
alignmentfiles=['./data/mini_nla_test.bam'],
o=None,
head=None,
bin=None,
binTag='DS',
byValue=None,
sliding=None,
bedfile='./data/mini_test.bed',
showtags=False,
featureTags=None,
joinedFeatureTags='reference_name',
sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False,
minMQ=0,
filterXA=False,
dedup=True,
divideMultimapping=False,
doNotDivideFragments=True,
contig=None,
r1only=False,
r2only=False,
blacklist=None,
filterMP=False,
splitFeatures=False,
feature_delimiter=',',
noNames=False) , return_df=True)
# !samtools view ./singlecellmultiomics/data/mini_nla_test.bam | grep 'RC:i:1' | wc -l
self.assertEqual( df.xs( 'test4',level='bname', drop_level=False).iloc[0].sum() , 1)
self.assertEqual( df.xs( 'test3',level='bname', drop_level=False).iloc[0].sum() , 383)
def test_byValue(self):
""" Test if the by value counting feature works, this counts the value of a feature instead of its presence"""
df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table(
SimpleNamespace(
alignmentfiles=['./data/mini_nla_test.bam'],
o=None,
head=None,
bin=30,
sliding=None,
binTag='DS',
byValue='RC',
bedfile=None,
showtags=False,
featureTags=None,
joinedFeatureTags='reference_name,RC',
sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False,
minMQ=0,
filterXA=False,
dedup=False,
divideMultimapping=False,
contig=None,
blacklist=None,
r1only=False,
r2only=False,
filterMP=False,
keepOverBounds=False,
doNotDivideFragments=True,
splitFeatures=False,
feature_delimiter=',',
noNames=False) , return_df=True)
self.assertEqual( df.sum(1).sum(), 765 )
self.assertEqual( df.loc[:,['A3-P15-1-1_25']].sum(skipna=True).sum(skipna=True), 12.0 )
def test_byValue_binned_autofill_joined(self):
""" Test if the by value counting feature works, this counts the value of a feature instead of its presence"""
df = singlecellmultiomics.bamProcessing.bamToCountTable.create_count_table(
SimpleNamespace(
alignmentfiles=['./data/mini_nla_test.bam'],
o=None,
head=None,
bin=30,
sliding=None,
binTag='DS',
byValue='RC',
bedfile=None,
showtags=False,
featureTags=None,
joinedFeatureTags='reference_name,RC',
sampleTags='SM', proper_pairs_only=False, no_indels=False, max_base_edits=None, no_softclips=False,
minMQ=0,
filterXA=False,
dedup=False,
divideMultimapping=False,
contig=None,
blacklist=None,
r1only=False,
r2only=False,
filterMP=False,
keepOverBounds=False,
doNotDivideFragments=True,
splitFeatures=False,
feature_delimiter=',',
noNames=False) , return_df=True)
self.assertEqual( df.sum(1).sum(), 765 )
self.assertEqual( df.loc[:,['A3-P15-1-1_25']].sum(skipna=True).sum(skipna=True), 12.0 )
if __name__ == '__main__':
unittest.main()
Datasets
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