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unittest_atac_seq_analysis.py
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unittest_atac_seq_analysis.py
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#!/usr/bin/env python
# import modules
import unittest
import os
from pandas.testing import assert_frame_equal
from numpy.testing import assert_array_almost_equal
import ohsu_atac as oa
import pandas as pd
import numpy as np
from sklearn.svm import SVC
from ohsu_atac import process_metadata
from ohsu_atac import normalize_matrix
from ohsu_atac import construct_svc
# define globals
TEST_DIR = os.path.dirname('test/')
TEST_METADATA_FILE = os.path.join(TEST_DIR,'test_metadata.txt')
TEST_ATACSEQ_FILE = os.path.join(TEST_DIR,'test_atac.csv')
# build unit tests
class TestEssentials(unittest.TestCase):
def setUp(self) -> None:
return super().setUp()
def tearDown(self) -> None:
return super().tearDown()
def test_read_metadata(self):
metadata = oa.read_metadata(TEST_METADATA_FILE)
metadata.columns = ['type']
metadata_compare = pd.DataFrame({
'type': ['excitatory_neuron','excitatory_neuron','inhibitory_neuron','excitatory_neuron','inhibitory_neuron']
})
assert_frame_equal(metadata,metadata_compare)
def test_process_metadata(self):
metadata = oa.read_metadata(TEST_METADATA_FILE)
df = pd.read_csv(TEST_ATACSEQ_FILE,index_col=0)
metadata = oa.process_metadata(df,metadata)
metadata_compare = pd.DataFrame(
{
'sample_id':['excitatory_neuron','excitatory_neuron','inhibitory_neuron','excitatory_neuron','inhibitory_neuron'],
'count':[3, 2, 5, 10, 3],
'std':[0.957427,1.000000,2.500000,3.785939,1.500000],
'mean':[0.75,0.50,1.25,2.50,0.75],
'zero':[2,3,3,2,3]
},
index=['GATTCGGTAGTTACGCAAGTCCAA','TGCGGCCTGATCATGAAGCTCGCT','GATTCGGTACCGGAAGCGTTAGAA','TGCGGCCTGCCGGAGCAGTTCAGG','ACGCGACGAATGATGCGATCTATC']
)
assert_frame_equal(metadata,metadata_compare)
def test_filter_matrix(self):
# define filter and comparisons
FILTER_THRESHOLD = 4
FILTERED_DF_COMPARE = pd.DataFrame(
{
'GATTCGGTACCGGAAGCGTTAGAA':[5, 0, 0, 0],
'TGCGGCCTGCCGGAGCAGTTCAGG':[8, 0, 0, 2]
},
index=['chr1-3094454-3095231','chr1-3117883-3118383','chr1-3119738-3120238','chr1-3120562-3121062']
)
FILTERED_METADATA_COMPARE = pd.DataFrame(
{
'sample_id':['inhibitory_neuron','excitatory_neuron'],
'count':[5, 10],
'std':[2.500000,3.785939],
'mean':[1.25,2.50],
'zero':[3, 2]
},
index=['GATTCGGTACCGGAAGCGTTAGAA','TGCGGCCTGCCGGAGCAGTTCAGG']
)
# read in and process parent files
metadata = oa.read_metadata(TEST_METADATA_FILE)
df = pd.read_csv(TEST_ATACSEQ_FILE,index_col=0)
metadata = oa.process_metadata(df,metadata)
# perform filter
filtered_df, filtered_metadata = oa.filter_matrix(df,metadata,filter=FILTER_THRESHOLD)
# confirm equality
assert_frame_equal(filtered_df,FILTERED_DF_COMPARE)
assert_frame_equal(filtered_metadata,FILTERED_METADATA_COMPARE)
def test_normalize_matrix(self):
# define comparison
NORMALIZED_DF_COMPARE = pd.DataFrame({
'GATTCGGTAGTTACGCAAGTCCAA': [2.0,0.0,1.0,0.0],
'TGCGGCCTGATCATGAAGCTCGCT': [3.0,0.0,0.0,0.0],
'GATTCGGTACCGGAAGCGTTAGAA': [3.0,0.0,0.0,0.0],
'TGCGGCCTGCCGGAGCAGTTCAGG': [2.4,0.0,0.0,0.6],
'ACGCGACGAATGATGCGATCTATC': [3.0,0.0,0.0,0.0]
},
index=['chr1-3094454-3095231','chr1-3117883-3118383','chr1-3119738-3120238','chr1-3120562-3121062']
)
# read in parent data and reduce size
df = pd.read_csv(TEST_ATACSEQ_FILE,index_col=0)
# normalize
normalized_df = oa.normalize_matrix(df)
assert_frame_equal(normalized_df,NORMALIZED_DF_COMPARE)
def test_boxcox_transformation(self):
TRANSFORMED_DF_COMPARE = np.array([[ 1.30893484,1.73205081,1.73205081,1.44265337,1.73205081],
[-0.97103679,-0.57735027,-0.57735027,-0.93273282,-0.57735027],
[ 0.63313874,-0.57735027,-0.57735027,-0.93273282,-0.57735027],
[-0.97103679,-0.57735027,-0.57735027,0.42281227,-0.57735027]])
df = pd.read_csv(TEST_ATACSEQ_FILE,index_col=0)
normalized_df = oa.normalize_matrix(df)
transformed_df = oa.boxcox_transformation(normalized_df)
assert_array_almost_equal(transformed_df,TRANSFORMED_DF_COMPARE)
def test_reduce_dimensions(self):
COMPONENTS_COMPARE = np.array([[ 3.57617221, -0.02896694],
[-1.5820112,-0.12770525],
[-0.9695218,1.12654213],
[-1.02463921,-0.96986995]])
df = pd.read_csv(TEST_ATACSEQ_FILE,index_col=0)
normalized_df = oa.normalize_matrix(df)
transformed_df = oa.boxcox_transformation(normalized_df)
components = oa.reduce_dimensions(transformed_df,n_components=2)
assert_array_almost_equal(components,COMPONENTS_COMPARE)
def test_generate_umap(self):
UMAP_COMPONENTS_COMPARE = np.array([[-73.724945,44.733227],
[-72.16196,44.814884],
[-72.917305,44.180145],
[-72.82937,45.469257]]).astype(np.float32)
df = pd.read_csv(TEST_ATACSEQ_FILE,index_col=0)
normalized_df = oa.normalize_matrix(df)
transformed_df = oa.boxcox_transformation(normalized_df)
components = oa.reduce_dimensions(transformed_df,n_components=2)
umap_components = oa.generate_umap(components,random_state=0,n_epochs=10)
assert_array_almost_equal(np.round(umap_components,2),np.round(UMAP_COMPONENTS_COMPARE,2))
def test_construct_svc(self):
pass
df = pd.read_csv(TEST_ATACSEQ_FILE,index_col=0)
normalized_df = oa.normalize_matrix(df)
transformed_df = oa.boxcox_transformation(normalized_df)
components = oa.reduce_dimensions(transformed_df,n_components=2)
umap_components = oa.generate_umap(components,random_state=0,n_epochs=10)
svc = oa.construct_svc()
if __name__ == '__main__':
unittest.main()