def test_aggregate_10x_matrices(self):
m1 = pg.read_input(
"tests/pegasus-test-data/input/heart_1k_v3/filtered_feature_bc_matrix.h5",
genome="mm10",
)
m2 = pg.read_input(
"tests/pegasus-test-data/input/heart_1k_v2/filtered_gene_bc_matrices_h5.h5",
genome="mm10",
)
pg.aggregate_matrices(
"tests/pegasus-test-data/input/aggregate_test.csv",
what_to_return='aggregate_test',
)
result = pg.read_input("aggregate_test.h5sc", genome="mm10")
self.assertEqual(m1.shape[0] + m2.shape[0], result.shape[0],
"Cell dimension is incorrect")
self.assertEqual(m1.shape[1], result.shape[1],
"Feature dimension is incorrect")
m1_result = result[list(range(m1.shape[0])), :]
m2_result = result[list(range(m1.shape[0], m1.shape[0] +
m2.shape[0])), :]
self.assertEqual((m1_result.X != m1.X).sum(), 0, "Values differ")
self.assertEqual((m2_result.X != m2.X).sum(), 0, "Values differ")
self.assertTrue(
m1_result.obs.index.values[0].startswith("heart_1k_v3"),
"Prefix not added")
self.assertTrue(
m2_result.obs.index.values[0].startswith("heart_1k_v2"),
"Prefix not added")
def test_multi_genome(self):
pg.aggregate_matrices(
"tests/pegasus-test-data/input/aggregate_multi_genome.csv",
what_to_return='aggregate_test',
)
f = h5py.File("aggregate_test.h5sc", "r")
self.assertIsNotNone(f["GRCh38"], "Genome not found")
self.assertIsNotNone(f["mm10"], "Genome not found")
with self.assertRaises(KeyError):
f["mm9"]
f.close()
def test_union(self):
m1 = anndata.AnnData(X=np.random.random((1, 3)),
var=pd.DataFrame(index=['a', 'b', 'c']),
obs=pd.DataFrame(index=['c1']))
pd.DataFrame(columns=m1.obs.index, index=m1.var.index,
data=m1.X.T).to_csv('test1_dge.txt.gz',
index_label='id',
sep='\t')
m2 = anndata.AnnData(X=np.random.random((1, 3)),
var=pd.DataFrame(index=['b', 'd', 'e']),
obs=pd.DataFrame(index=['c1']))
pd.DataFrame(columns=m2.obs.index, index=m2.var.index,
data=m2.X.T).to_csv('test2_dge.txt.gz',
index_label='id',
sep='\t')
pd.DataFrame(
data=dict(Sample=['s1', 's2'],
Location=['test1_dge.txt.gz', 'test2_dge.txt.gz'],
Reference=['a', 'a'])).to_csv('aggregate_test.csv',
index=False)
result = pg.aggregate_matrices("aggregate_test.csv",
what_to_return="AnnData")
self.diff_m1_m2(result, m1, m2)