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Support for sparse tiledb arrays for the X matrix (#1496)

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Support for sparse tiledb arrays for the X matrix

1. cxgtool can now output sparse matrices
2. cxg_adaptor and diffexp_cxg updated to handle sparse matrices
3. added a test in test_diffexp to test sparse diffexp and get_X_array
This commit is contained in:
bmccandless
2020-05-28 18:36:02 -07:00
committed by GitHub
parent 030eea1898
commit f7585eef1e
5 changed files with 283 additions and 55 deletions
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server/test/test_diffexp.py
+47 -11
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@@ -1,11 +1,13 @@
import unittest
from server.data_common.matrix_loader import MatrixDataLoader
from server.common.app_config import AppConfig
from server.test import PROJECT_ROOT, app_config
import server.compute.diffexp_cxg as diffexp_cxg
import server.compute.diffexp_generic as diffexp_generic
from server.converters.cxgtool import write_cxg
import numpy as np
from server.test import PROJECT_ROOT
import tempfile
import anndata
import os
class DiffExpTest(unittest.TestCase):
@@ -13,12 +15,9 @@ class DiffExpTest(unittest.TestCase):
adaptor types and different algorithms."""
def load_dataset(self, path):
app_config = AppConfig()
app_config.single_dataset__datapath = path
app_config.server__verbose = True
app_config.complete_config()
config = app_config(path)
loader = MatrixDataLoader(path)
adaptor = loader.open(app_config)
adaptor = loader.open(config)
return adaptor
def get_mask(self, adaptor, start, stride):
@@ -46,9 +45,14 @@ class DiffExpTest(unittest.TestCase):
self.assertEqual(len(results), len(expects))
for result, expect in zip(results, expects):
self.assertEqual(result[0], expect[0])
self.assertAlmostEqual(result[1], expect[1])
self.assertAlmostEqual(result[2], expect[2])
self.assertAlmostEqual(result[3], expect[3])
self.assertTrue(np.isclose(result[1], expect[1], 1e-6, 1e-6))
self.assertTrue(np.isclose(result[2], expect[2], 1e-6, 1e-6))
self.assertTrue(np.isclose(result[3], expect[3], 1e-6, 1e-6))
def get_X_col(self, adaptor, cols):
varmask = np.zeros(adaptor.get_shape()[1], dtype=bool)
varmask[cols] = True
return adaptor.get_X_array(None, varmask)
def test_anndata_default(self):
"""Test an anndata adaptor with its default diffexp algorithm (diffexp_generic)"""
@@ -80,3 +84,35 @@ class DiffExpTest(unittest.TestCase):
# run it directly
results = diffexp_generic.diffexp_ttest(adaptor, maskA, maskB, 10)
self.check_1_10_2_10(results)
def test_cxg_sparse(self):
with tempfile.TemporaryDirectory() as dirname:
sparsename = os.path.join(dirname, "sparse.cxg")
densename = os.path.join(dirname, "dense.cxg")
source_h5ad = anndata.read_h5ad(f"{PROJECT_ROOT}/example-dataset/pbmc3k.h5ad")
# create a cxg sparse array
write_cxg(adata=source_h5ad, container=sparsename, title="pbmc3k", sparse_threshold=100)
write_cxg(adata=source_h5ad, container=densename, title="pbmc3k", sparse_threshold=0)
adaptor_sparse = self.load_dataset(sparsename)
adaptor_dense = self.load_dataset(densename)
col_results = []
for adaptor in (adaptor_sparse, adaptor_dense):
maskA = self.get_mask(adaptor, 1, 10)
maskB = self.get_mask(adaptor, 2, 10)
diffexp_results = diffexp_cxg.diffexp_ttest(adaptor, maskA, maskB, 10)
self.check_1_10_2_10(diffexp_results)
topcols = [x[0] for x in diffexp_results]
cols = self.get_X_col(adaptor, topcols)
assert cols.shape[0] == adaptor.get_shape()[0]
assert cols.shape[1] == len(diffexp_results)
col_results.append(cols)
x = adaptor.get_X_array()
print(x)
for row in range(col_results[0].shape[0]):
for col in range(col_results[0].shape[1]):
sval = col_results[0][row][col]
dval = col_results[1][row][col]
self.assertTrue(np.isclose(sval, dval, 1e-6, 1e-6))