def test_run_correlation_OTUs(self):
"""run_correlation_OTUs works"""
category_info = {'sample1':'0.1',
'sample2':'0.2',
'sample3':'0.3',
'sample4':'0.4'}
OTU_sample_info = {'0': {'sample1': '0', 'sample2': '1', 'sample3': '2', 'sample4': '3'},
'1': {'sample1': '7', 'sample2': '5', 'sample3': '3', 'sample4': '1'},
'2': {'sample1': '4', 'sample2': '4.2', 'sample3': '4', 'sample4': '4'},
'3': {'sample1': '0', 'sample2': '1', 'sample3': '0', 'sample4': '1'}}
OTU_list = ['1', '0', '2', '3']
result = run_correlation_OTUs(OTU_list, category_info, OTU_sample_info)
#OTU 0 should be positively correlated, 1 negative, and 2&3 neutral
self.assertFloatEqual(result['0'][0], 1.0)#r
self.assertFloatEqual(result['0'][1], 0.0)#prob
self.assertEqual(result['0'][2], "[3.0, 0.0, 2.0, 1.0]")
self.assertEqual(result['0'][3], "[0.40000000000000002, 0.10000000000000001, 0.29999999999999999, 0.20000000000000001]")
self.assertFloatEqual(result['1'], [-0.99999999999999956, 4.4408920985006281e-16, '[1.0, 7.0, 3.0, 5.0]', '[0.40000000000000002, 0.10000000000000001, 0.29999999999999999, 0.20000000000000001]'])
#test that appropriate error is raised is categorical
category_info = {'sample1':'A',
'sample2':'B',
'sample3':'A',
'sample4':'B'}
self.assertRaises(ValueError, run_correlation_OTUs, OTU_list, category_info, OTU_sample_info)
def test_run_correlation_OTUs(self):
"""run_single_correlation works"""
category_info = {
'sample1': '0.1',
'sample2': '0.2',
'sample3': '0.3',
'sample4': '0.4'
}
OTU_sample_info = {
'0': {
'sample1': '0',
'sample2': '1',
'sample3': '2',
'sample4': '3'
},
'1': {
'sample1': '7',
'sample2': '5',
'sample3': '3',
'sample4': '1'
},
'2': {
'sample1': '4',
'sample2': '4.2',
'sample3': '4',
'sample4': '4'
},
'3': {
'sample1': '0',
'sample2': '1',
'sample3': '0',
'sample4': '1'
}
}
OTU_list = ['1', '0', '2', '3']
result = run_correlation_OTUs(OTU_list, category_info, OTU_sample_info)
#OTU 0 should be positively correlated, 1 negative, and 2&3 neutral
self.assertFloatEqual(result['0'], [1.0, 0.0, 0.0])
self.assertFloatEqual(result['1'], [
-0.99999999999999956, 4.4408920985006281e-16,
1.7763568394002513e-15
])
self.assertFloatEqual(
result['2'],
[-0.25819888974715061, 0.74180111025284945, 2.9672044410113978])
self.assertFloatEqual(
result['3'],
[0.44721359549995815, 0.55278640450004191, 2.2111456180001676])
#test that appropriate error is raised is categorical
category_info = {
'sample1': 'A',
'sample2': 'B',
'sample3': 'A',
'sample4': 'B'
}
self.assertRaises(ValueError, run_correlation_OTUs, OTU_list,
category_info, OTU_sample_info)
def test_output_results_correlation(self):
"""output_results_correlation works"""
category_info = {
'sample1': '0.1',
'sample2': '0.2',
'sample3': '0.3',
'sample4': '0.4'
}
OTU_sample_info = {
'0': {
'sample1': '0',
'sample2': '1',
'sample3': '2',
'sample4': '3'
},
'1': {
'sample1': '7',
'sample2': '5',
'sample3': '3',
'sample4': '1'
},
'2': {
'sample1': '4',
'sample2': '4.2',
'sample3': '4',
'sample4': '4'
},
'3': {
'sample1': '0',
'sample2': '1',
'sample3': '0',
'sample4': '1'
}
}
taxonomy_info = {
'0': 'taxon1',
'1': 'taxon2',
'2': 'taxon3',
'3': 'taxon4'
}
OTU_list = ['1', '0', '2', '3']
result = run_correlation_OTUs(OTU_list, category_info, OTU_sample_info)
output = output_results_correlation(result, taxonomy_info)
self.assertEqual(
output[0],
'OTU\tprob\tBonferroni_corrected\tFDR_corrected\tr\tConsensus Lineage'
)
self.assertEqual(
output[1],
'1\t4.4408920985e-16\t1.7763568394e-15\t8.881784197e-16\t-1.0\ttaxon2'
)
self.assertEqual(len(output), 5)
def test_output_results_correlation(self):
"""output_results_correlation works"""
category_info = {"sample1": "0.1", "sample2": "0.2", "sample3": "0.3", "sample4": "0.4"}
OTU_sample_info = {
"0": {"sample1": "0", "sample2": "1", "sample3": "2", "sample4": "3"},
"1": {"sample1": "7", "sample2": "5", "sample3": "3", "sample4": "1"},
"2": {"sample1": "4", "sample2": "4.2", "sample3": "4", "sample4": "4"},
"3": {"sample1": "0", "sample2": "1", "sample3": "0", "sample4": "1"},
}
taxonomy_info = {"0": "taxon1", "1": "taxon2", "2": "taxon3", "3": "taxon4"}
OTU_list = ["1", "0", "2", "3"]
result = run_correlation_OTUs(OTU_list, category_info, OTU_sample_info)
output = output_results_correlation(result, taxonomy_info)
self.assertEqual(output[0], "OTU\tprob\tBonferroni_corrected\tFDR_corrected\tr\tConsensus Lineage")
self.assertEqual(output[1], "1\t4.4408920985e-16\t1.7763568394e-15\t8.881784197e-16\t-1.0\ttaxon2")
self.assertEqual(len(output), 5)
def test_run_correlation_OTUs(self):
"""run_single_correlation works"""
category_info = {"sample1": "0.1", "sample2": "0.2", "sample3": "0.3", "sample4": "0.4"}
OTU_sample_info = {
"0": {"sample1": "0", "sample2": "1", "sample3": "2", "sample4": "3"},
"1": {"sample1": "7", "sample2": "5", "sample3": "3", "sample4": "1"},
"2": {"sample1": "4", "sample2": "4.2", "sample3": "4", "sample4": "4"},
"3": {"sample1": "0", "sample2": "1", "sample3": "0", "sample4": "1"},
}
OTU_list = ["1", "0", "2", "3"]
result = run_correlation_OTUs(OTU_list, category_info, OTU_sample_info)
# OTU 0 should be positively correlated, 1 negative, and 2&3 neutral
self.assertFloatEqual(result["0"], [1.0, 0.0, 0.0])
self.assertFloatEqual(result["1"], [-0.99999999999999956, 4.4408920985006281e-16, 1.7763568394002513e-15])
self.assertFloatEqual(result["2"], [-0.25819888974715061, 0.74180111025284945, 2.9672044410113978])
self.assertFloatEqual(result["3"], [0.44721359549995815, 0.55278640450004191, 2.2111456180001676])
# test that appropriate error is raised is categorical
category_info = {"sample1": "A", "sample2": "B", "sample3": "A", "sample4": "B"}
self.assertRaises(ValueError, run_correlation_OTUs, OTU_list, category_info, OTU_sample_info)
def test_output_results_correlation(self):
"""output_results_correlation works"""
category_info = {'sample1':'0.1',
'sample2':'0.2',
'sample3':'0.3',
'sample4':'0.4'}
OTU_sample_info = {'0': {'sample1': '0', 'sample2': '1', 'sample3': '2', 'sample4': '3'},
'1': {'sample1': '7', 'sample2': '5', 'sample3': '3', 'sample4': '1'},
'2': {'sample1': '4', 'sample2': '4.2', 'sample3': '4', 'sample4': '4'},
'3': {'sample1': '0', 'sample2': '1', 'sample3': '0', 'sample4': '1'}}
taxonomy_info = {'0': 'taxon1',
'1': 'taxon2',
'2': 'taxon3',
'3': 'taxon4'}
OTU_list = ['1', '0', '2', '3']
result = run_correlation_OTUs(OTU_list, category_info, OTU_sample_info)
output = output_results_correlation(result, taxonomy_info)
self.assertEqual(output[0], 'OTU\tprob\totu_values_y\tcat_values_x\tBonferroni_corrected\tFDR_corrected\tr\tConsensus Lineage')
self.assertEqual(output[1], '1\t4.4408920985e-16\t[1.0, 7.0, 3.0, 5.0]\t[0.40000000000000002, 0.10000000000000001, 0.29999999999999999, 0.20000000000000001]\t1.7763568394e-15\t8.881784197e-16\t-1.0\ttaxon2')
self.assertEqual(len(output), 5)
