def test_bad_values_fail(self): # Just confirm that the machinery works, anything more specific is just # restating the _valid_inputs dict which is more declarative than a # unit-test anyways. with self.assertRaisesRegex(ValueError, 'trunc_len'): denoise_single(self.demux_seqs, -1) with self.assertRaisesRegex(ValueError, 'n_reads_learn'): denoise_single(self.demux_seqs, 100, n_reads_learn=0)
def test_bad_values_fail(self): # Just confirm that the machinery works, anything more specific is just # restating the _valid_inputs dict which is more declarative than a # unit-test anyways. with self.assertRaisesRegex(ValueError, 'trunc_len_f'): denoise_paired(self.demux_seqs, -1, 150) with self.assertRaisesRegex(ValueError, 'trunc_len_r'): denoise_paired(self.demux_seqs, 150, -1) with self.assertRaisesRegex(ValueError, 'n_reads_learn'): denoise_paired(self.demux_seqs, 150, 150, n_reads_learn=0) with self.assertRaisesRegex(ValueError, 'consensus'): denoise_single(self.demux_seqs, 150, 150, chimera_method='foo')
def test_override(self): with open(self.get_data_path('expected/single-override.tsv')) as fh: exp_table = biom.Table.from_tsv(fh, None, None, lambda x: x) exp_rep_seqs = list( skbio.io.read(self.get_data_path('expected/single-override.fasta'), 'fasta', constructor=skbio.DNA)) for seq in exp_rep_seqs: del seq.metadata['description'] exp_md = qiime2.Metadata.load( self.get_data_path('expected/single-override-stats.tsv')) # NOTE: the test data isn't interesting enough to be impacted by # min_fold_parent_over_abundance. table, rep_seqs, md = denoise_single( self.demux_seqs, 100, trim_left=10, max_ee=10.5, trunc_q=1, n_threads=1, n_reads_learn=2, hashed_feature_ids=False, chimera_method='consensus', min_fold_parent_over_abundance=1.1) self.assertEqual(table, exp_table) self.assertEqual(_sort_seqs(rep_seqs), _sort_seqs(exp_rep_seqs)) self.assertEqual(md, exp_md)
def test_underscore_samples(self): self.demux_seqs = SingleLanePerSampleSingleEndFastqDirFmt( self.get_data_path('underscore_samples'), 'r') with open(self.get_data_path('expected/underscore-samples.tsv')) as fh: exp_table = biom.Table.from_tsv(fh, None, None, lambda x: x) exp_rep_seqs = list( skbio.io.read( self.get_data_path('expected/underscore-samples.fasta'), 'fasta', constructor=skbio.DNA)) for seq in exp_rep_seqs: del seq.metadata['description'] exp_md = qiime2.Metadata.load( self.get_data_path('expected/underscore-samples-stats.tsv')) # Historical NOTE: default used to be `pooled`, so the data still # expects that. Since this is only testing underscores, it shouldn't # matter much and serves as a regression test to boot. table, rep_seqs, md = denoise_single(self.demux_seqs, 100, chimera_method='pooled') self.assertEqual(table, exp_table) self.assertEqual(_sort_seqs(rep_seqs), _sort_seqs(exp_rep_seqs)) self.assertEqual(md, exp_md)
def test_defaults(self): with open(self.get_data_path('expected/single-default.tsv')) as fh: exp_table = biom.Table.from_tsv(fh, None, None, lambda x: x) exp_rep_seqs = list( skbio.io.read(self.get_data_path('expected/single-default.fasta'), 'fasta', constructor=skbio.DNA)) for seq in exp_rep_seqs: del seq.metadata['description'] table, rep_seqs = denoise_single(self.demux_seqs, 100) self.assertEqual(table, exp_table) self.assertEqual(_sort_seqs(rep_seqs), _sort_seqs(exp_rep_seqs))
def test_override(self): with open(self.get_data_path('expected/single-override.tsv')) as fh: exp_table = biom.Table.from_tsv(fh, None, None, lambda x: x) exp_rep_seqs = list( skbio.io.read(self.get_data_path('expected/single-override.fasta'), 'fasta', constructor=skbio.DNA)) for seq in exp_rep_seqs: del seq.metadata['description'] table, rep_seqs = denoise_single( self.demux_seqs, 100, trim_left=10, max_ee=10.5, trunc_q=1, n_threads=0, n_reads_learn=2, hashed_feature_ids=False) self.assertEqual(table, exp_table) self.assertEqual(_sort_seqs(rep_seqs), _sort_seqs(exp_rep_seqs))
def test_underscore_samples(self): self.demux_seqs = SingleLanePerSampleSingleEndFastqDirFmt( self.get_data_path('underscore_samples'), 'r') with open(self.get_data_path('expected/underscore-samples.tsv')) as fh: exp_table = biom.Table.from_tsv(fh, None, None, lambda x: x) exp_rep_seqs = list( skbio.io.read( self.get_data_path('expected/underscore-samples.fasta'), 'fasta', constructor=skbio.DNA)) for seq in exp_rep_seqs: del seq.metadata['description'] table, rep_seqs = denoise_single(self.demux_seqs, 100) self.assertEqual(table, exp_table) self.assertEqual(_sort_seqs(rep_seqs), _sort_seqs(exp_rep_seqs))
def test_no_chimera_method(self): with open(self.get_data_path('expected/single-default.tsv')) as fh: exp_table = biom.Table.from_tsv(fh, None, None, lambda x: x) exp_rep_seqs = list( skbio.io.read(self.get_data_path('expected/single-default.fasta'), 'fasta', constructor=skbio.DNA)) for seq in exp_rep_seqs: del seq.metadata['description'] exp_md = qiime2.Metadata.load( self.get_data_path('expected/single-default-stats.tsv')) table, rep_seqs, md = denoise_single(self.demux_seqs, 100, chimera_method='none') self.assertEqual(table, exp_table) self.assertEqual(_sort_seqs(rep_seqs), _sort_seqs(exp_rep_seqs)) self.assertEqual(md, exp_md)
def test_override(self): with open(self.get_data_path('expected/single-override.tsv')) as fh: exp_table = biom.Table.from_tsv(fh, None, None, lambda x: x) exp_rep_seqs = list( skbio.io.read(self.get_data_path('expected/single-override.fasta'), 'fasta', constructor=skbio.DNA)) for seq in exp_rep_seqs: del seq.metadata['description'] # NOTE: the test data isn't interesting enough to be impacted by # min_fold_parent_over_abundance. table, rep_seqs = denoise_single( self.demux_seqs, 100, trim_left=10, max_ee=10.5, trunc_q=1, n_threads=0, n_reads_learn=2, hashed_feature_ids=False, chimera_method='consensus', min_fold_parent_over_abundance=1.1) self.assertEqual(table, exp_table) self.assertEqual(_sort_seqs(rep_seqs), _sort_seqs(exp_rep_seqs))
def test_underscore_samples(self): self.demux_seqs = SingleLanePerSampleSingleEndFastqDirFmt( self.get_data_path('underscore_samples'), 'r') with open(self.get_data_path('expected/underscore-samples.tsv')) as fh: exp_table = biom.Table.from_tsv(fh, None, None, lambda x: x) exp_rep_seqs = list( skbio.io.read( self.get_data_path('expected/underscore-samples.fasta'), 'fasta', constructor=skbio.DNA)) for seq in exp_rep_seqs: del seq.metadata['description'] # Historical NOTE: default used to be `pooled`, so the data still # expects that. Since this is only testing underscores, it shouldn't # matter much and serves as a regression test to boot. table, rep_seqs = denoise_single(self.demux_seqs, 100, chimera_method='pooled') self.assertEqual(table, exp_table) self.assertEqual(_sort_seqs(rep_seqs), _sort_seqs(exp_rep_seqs))
def test_trim_left_bigger_than_trunc_len(self): with self.assertRaisesRegex(ValueError, 'trim_left'): denoise_single(self.demux_seqs, 100, trim_left=100) # Shouldn't fail when `trunc_len=0` denoise_single(self.demux_seqs, 0, trim_left=100)
def test_all_reads_filtered(self): with self.assertRaisesRegex(ValueError, 'filter'): denoise_single(self.demux_seqs, 10000)