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https://github.com/chanzuckerberg/cellxgene.git
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f7585eef1e
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
119 lines
5.1 KiB
Python
119 lines
5.1 KiB
Python
import unittest
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from server.data_common.matrix_loader import MatrixDataLoader
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from server.test import PROJECT_ROOT, app_config
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import server.compute.diffexp_cxg as diffexp_cxg
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import server.compute.diffexp_generic as diffexp_generic
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from server.converters.cxgtool import write_cxg
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import numpy as np
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import tempfile
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import anndata
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import os
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class DiffExpTest(unittest.TestCase):
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"""Tests the diffexp returns the expected results for one test case, using different
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adaptor types and different algorithms."""
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def load_dataset(self, path):
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config = app_config(path)
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loader = MatrixDataLoader(path)
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adaptor = loader.open(config)
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return adaptor
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def get_mask(self, adaptor, start, stride):
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"""Simple function to return a mask or rows"""
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rows = adaptor.get_shape()[0]
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sel = list(range(start, rows, stride))
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mask = np.zeros(rows, dtype=bool)
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mask[sel] = True
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return mask
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def check_1_10_2_10(self, results):
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"""Checks the results for a specific set of rows selections"""
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expects = [
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[956, 0.016060986, 0.0008649321884808977, 1.0],
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[1124, 0.96602094, 0.0011717216548271284, 1.0],
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[1809, 1.1110606, 0.0019304405196777848, 1.0],
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[1712, -0.5525154, 0.0051788902660723345, 1.0],
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[1754, 0.5201581, 0.005691734062127954, 1.0],
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[948, 1.6390722, 0.006622111055981219, 1.0],
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[1810, 0.78618884, 0.007055917428377063, 1.0],
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[779, 1.5241305, 0.007202934422407284, 1.0],
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[1575, 1.0317602, 0.007830310753043345, 1.0],
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[576, 0.97873515, 0.008272092578813124, 1.0],
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]
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self.assertEqual(len(results), len(expects))
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for result, expect in zip(results, expects):
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self.assertEqual(result[0], expect[0])
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self.assertTrue(np.isclose(result[1], expect[1], 1e-6, 1e-6))
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self.assertTrue(np.isclose(result[2], expect[2], 1e-6, 1e-6))
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self.assertTrue(np.isclose(result[3], expect[3], 1e-6, 1e-6))
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def get_X_col(self, adaptor, cols):
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varmask = np.zeros(adaptor.get_shape()[1], dtype=bool)
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varmask[cols] = True
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return adaptor.get_X_array(None, varmask)
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def test_anndata_default(self):
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"""Test an anndata adaptor with its default diffexp algorithm (diffexp_generic)"""
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adaptor = self.load_dataset(f"{PROJECT_ROOT}/example-dataset/pbmc3k.h5ad")
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maskA = self.get_mask(adaptor, 1, 10)
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maskB = self.get_mask(adaptor, 2, 10)
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results = adaptor.compute_diffexp_ttest(maskA, maskB, 10)
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self.check_1_10_2_10(results)
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def test_cxg_default(self):
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"""Test a cxg adaptor with its default diffexp algorithm (diffexp_cxg)"""
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adaptor = self.load_dataset(f"{PROJECT_ROOT}/server/test/test_datasets/pbmc3k.cxg")
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maskA = self.get_mask(adaptor, 1, 10)
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maskB = self.get_mask(adaptor, 2, 10)
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# run it through the adaptor
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results = adaptor.compute_diffexp_ttest(maskA, maskB, 10)
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self.check_1_10_2_10(results)
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# run it directly
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results = diffexp_cxg.diffexp_ttest(adaptor, maskA, maskB, 10)
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self.check_1_10_2_10(results)
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def test_cxg_generic(self):
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"""Test a cxg adaptor with the generic adaptor"""
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adaptor = self.load_dataset(f"{PROJECT_ROOT}/server/test/test_datasets/pbmc3k.cxg")
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maskA = self.get_mask(adaptor, 1, 10)
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maskB = self.get_mask(adaptor, 2, 10)
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# run it directly
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results = diffexp_generic.diffexp_ttest(adaptor, maskA, maskB, 10)
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self.check_1_10_2_10(results)
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def test_cxg_sparse(self):
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with tempfile.TemporaryDirectory() as dirname:
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sparsename = os.path.join(dirname, "sparse.cxg")
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densename = os.path.join(dirname, "dense.cxg")
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source_h5ad = anndata.read_h5ad(f"{PROJECT_ROOT}/example-dataset/pbmc3k.h5ad")
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# create a cxg sparse array
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write_cxg(adata=source_h5ad, container=sparsename, title="pbmc3k", sparse_threshold=100)
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write_cxg(adata=source_h5ad, container=densename, title="pbmc3k", sparse_threshold=0)
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adaptor_sparse = self.load_dataset(sparsename)
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adaptor_dense = self.load_dataset(densename)
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col_results = []
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for adaptor in (adaptor_sparse, adaptor_dense):
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maskA = self.get_mask(adaptor, 1, 10)
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maskB = self.get_mask(adaptor, 2, 10)
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diffexp_results = diffexp_cxg.diffexp_ttest(adaptor, maskA, maskB, 10)
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self.check_1_10_2_10(diffexp_results)
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topcols = [x[0] for x in diffexp_results]
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cols = self.get_X_col(adaptor, topcols)
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assert cols.shape[0] == adaptor.get_shape()[0]
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assert cols.shape[1] == len(diffexp_results)
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col_results.append(cols)
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x = adaptor.get_X_array()
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print(x)
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for row in range(col_results[0].shape[0]):
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for col in range(col_results[0].shape[1]):
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sval = col_results[0][row][col]
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dval = col_results[1][row][col]
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self.assertTrue(np.isclose(sval, dval, 1e-6, 1e-6))
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