def test_10X_zip_not_a_file():
utils.assert_raises_message(
FileNotFoundError,
"No such file: 'not_a_file.zip'",
scprep.io.load_10X_zip,
"not_a_file.zip",
)
def test_10X_zip_url_404():
utils.assert_raises_message(
urllib.error.HTTPError,
"HTTP Error 404: Not Found",
scprep.io.load_10X_zip,
"https://github.com/KrishnaswamyLab/scprep/invalid_url",
)
def test_10X_zip_url_not_a_zip():
utils.assert_raises_message(
zipfile.BadZipFile,
"File is not a zip file",
scprep.io.load_10X_zip,
"https://github.com/KrishnaswamyLab/scprep/raw/master/data/test_data/test_10X",
)
def test_10X_HDF5_invalid_backend():
h5_file = os.path.join(data.data_dir, "test_10X.h5")
utils.assert_raises_message(
ValueError,
"Expected backend in ['tables', 'h5py']. Got invalid",
scprep.io.load_10X_HDF5,
filename=h5_file,
backend="invalid",
)
def test_10X_HDF5_invalid_gene_labels():
h5_file = os.path.join(data.data_dir, "test_10X.h5")
utils.assert_raises_message(
ValueError,
"gene_labels='invalid' not recognized. "
"Choose from ['symbol', 'id', 'both']",
scprep.io.load_10X_HDF5,
filename=h5_file,
gene_labels="invalid",
)
def test_10X_HDF5_invalid_genome():
h5_file = os.path.join(data.data_dir, "test_10X.h5")
utils.assert_raises_message(
ValueError,
"Genome invalid not found in {}. "
"Available genomes: GRCh38".format(h5_file),
scprep.io.load_10X_HDF5,
filename=h5_file,
genome="invalid",
)
def test_fcs_naming_error():
path = fcsparser.test_sample_path
utils.assert_raises_message(
ValueError,
"Expected channel_naming in ['$PnS', '$PnN']. "
"Got 'invalid'",
scprep.io.load_fcs,
path,
override=True,
channel_naming="invalid",
)
def test_fcs_file_error():
utils.assert_raises_message(
RuntimeError,
"fcsparser failed to load {}, likely due to"
" a malformed header. You can try using "
"`override=True` to use scprep's built-in "
"experimental FCS parser.".format(
os.path.join(data.data_dir, "test_small.csv")),
scprep.io.load_fcs,
os.path.join(data.data_dir, "test_small.csv"),
)
def test_10X_HDF5_genome_cellranger3():
h5_file = os.path.join(data.data_dir, "test_10X_cellranger3.h5")
utils.assert_raises_message(
NotImplementedError,
"Selecting genomes for Cellranger 3.0 files is not "
"currently supported. Please file an issue at "
"https://github.com/KrishnaswamyLab/scprep/issues",
scprep.io.load_10X_HDF5,
filename=h5_file,
genome="GRCh38",
)
def test_parse_header():
header1 = np.arange(10)
header2 = os.path.join(data.data_dir, "gene_symbols.csv")
utils.assert_raises_message(
ValueError,
"Expected 5 entries in gene_names. Got 10",
scprep.io.utils._parse_header,
header1,
5,
)
utils.assert_raises_message(
ValueError,
"Expected 50 entries in {}. Got 100".format(os.path.abspath(header2)),
scprep.io.utils._parse_header,
header2,
50,
)
def test_10X_zip_error():
filename = os.path.join(data.data_dir, "test_10X.zip")
utils.assert_raises_message(
ValueError,
"gene_labels='invalid' not recognized. "
"Choose from ['symbol', 'id', 'both']",
scprep.io.load_10X_zip,
filename,
gene_labels="invalid",
)
utils.assert_raises_message(
ValueError,
"Expected a single zipped folder containing 'matrix.mtx(.gz)', "
"'[genes/features].tsv(.gz)', and 'barcodes.tsv(.gz)'. Got ",
scprep.io.load_10X_zip,
os.path.join(data.data_dir, "test_10X_invalid.zip"),
)
def test_mtx():
X = data.load_10X()
filename = os.path.join(data.data_dir, "test_10X", "matrix.mtx.gz")
X_mtx = scprep.io.load_mtx(
filename,
gene_names=os.path.join(data.data_dir, "gene_symbols.csv"),
cell_names=os.path.join(data.data_dir, "barcodes.tsv"),
cell_axis="column",
)
assert np.sum(np.sum(X.to_numpy() != X_mtx.to_numpy())) == 0
np.testing.assert_array_equal(X.columns, X_mtx.columns)
np.testing.assert_array_equal(X.index, X_mtx.index)
assert scprep.utils.is_sparse_dataframe(X_mtx)
X_mtx = scprep.io.load_mtx(filename,
gene_names=X.columns,
cell_names=X.index,
cell_axis="column")
assert np.sum(np.sum(X.to_numpy() != X_mtx.to_numpy())) == 0
np.testing.assert_array_equal(X.columns, X_mtx.columns)
np.testing.assert_array_equal(X.index, X_mtx.index)
assert scprep.utils.is_sparse_dataframe(X_mtx)
X_mtx = scprep.io.load_mtx(filename,
gene_names=None,
cell_names=None,
sparse=False,
cell_axis="column")
assert np.sum(np.sum(X.to_numpy() != X_mtx)) == 0
assert isinstance(X_mtx, np.ndarray)
utils.assert_raises_message(
ValueError,
"cell_axis neither not recognized. "
"Expected 'row' or 'column'",
scprep.io.load_mtx,
filename,
cell_axis="neither",
)
X_mtx = scprep.io.load_mtx(
filename,
gene_names=np.arange(X.shape[1]).astype("str"),
cell_names=np.arange(X.shape[0]),
)
assert X_mtx.shape == (100, 100)
assert scprep.utils.is_sparse_dataframe(X_mtx)
assert X_mtx.columns[0] == "0"
assert X_mtx.index[0] == 0
def test_fcs_header_error():
path = fcsparser.test_sample_path
meta, data = fcsparser.parse(path,
reformat_meta=True,
channel_naming="$PnN")
meta_bad = copy.deepcopy(meta)
meta_bad["$DATASTART"] = meta_bad["__header__"]["data start"]
meta_bad["$DATAEND"] = meta_bad["__header__"]["data end"]
meta_bad["__header__"]["data start"] = 0
meta_bad["__header__"]["data end"] = 0
assert (scprep.io.fcs._parse_fcs_header(meta_bad)["$DATASTART"] ==
scprep.io.fcs._parse_fcs_header(meta)["$DATASTART"])
assert (scprep.io.fcs._parse_fcs_header(meta_bad)["$DATAEND"] ==
scprep.io.fcs._parse_fcs_header(meta)["$DATAEND"])
meta_bad = copy.deepcopy(meta)
meta_bad["$DATATYPE"] = "invalid"
utils.assert_raises_message(
ValueError,
"Expected $DATATYPE in ['F', 'D']. "
"Got 'invalid'",
scprep.io.fcs._parse_fcs_header,
meta_bad,
)
meta_bad = copy.deepcopy(meta)
for byteord, endian in zip(["4,3,2,1", "1,2,3,4"], [">", "<"]):
meta_bad["$BYTEORD"] = byteord
assert scprep.io.fcs._parse_fcs_header(meta_bad)["$ENDIAN"] == endian
meta_bad["$BYTEORD"] = "invalid"
utils.assert_raises_message(
ValueError,
"Expected $BYTEORD in ['1,2,3,4', '4,3,2,1']. "
"Got 'invalid'",
scprep.io.fcs._parse_fcs_header,
meta_bad,
)
def test_10X():
X = data.load_10X()
assert X.shape == (100, 100)
assert scprep.utils.is_sparse_dataframe(X)
assert X.columns[0] == "Arl8b"
X = data.load_10X(gene_labels="id", sparse=False)
assert X.shape == (100, 100)
assert isinstance(X, pd.DataFrame)
assert not scprep.utils.is_sparse_dataframe(X)
assert X.columns[0] == "ENSMUSG00000030105"
X = data.load_10X(gene_labels="both")
assert X.shape == (100, 100)
assert scprep.utils.is_sparse_dataframe(X)
assert X.columns[0] == "Arl8b (ENSMUSG00000030105)"
X_cellranger3 = scprep.io.load_10X(os.path.join(data.data_dir,
"test_10X_cellranger3"),
gene_labels="both")
np.testing.assert_array_equal(X.index, X_cellranger3.index)
np.testing.assert_array_equal(X.columns, X_cellranger3.columns)
np.testing.assert_array_equal(X.index, X_cellranger3.index)
utils.assert_raises_message(
ValueError,
"gene_labels='invalid' not recognized. "
"Choose from ['symbol', 'id', 'both']",
data.load_10X,
gene_labels="invalid",
)
utils.assert_raises_message(
FileNotFoundError,
"{} is not a directory".format(
os.path.join(data.data_dir, "test_10X.zip")),
scprep.io.load_10X,
os.path.join(data.data_dir, "test_10X.zip"),
)
utils.assert_raises_message(
FileNotFoundError,
"'matrix.mtx(.gz)', '[genes/features].tsv(.gz)', and 'barcodes.tsv(.gz)' must be present "
"in {}".format(data.data_dir),
scprep.io.load_10X,
data.data_dir,
)
def test_csv_and_tsv():
X = data.load_10X()
filename = os.path.join(data.data_dir, "test_small.csv")
X_csv = scprep.io.load_csv(os.path.join(data.data_dir, "test_small.csv"),
gene_names=True,
cell_names=True)
with utils.assert_warns_message(
RuntimeWarning,
"Duplicate cell names detected! Some functions may not work as intended. "
"You can fix this by running `scprep.sanitize.check_index(data)`.",
):
scprep.io.load_csv(
os.path.join(data.data_dir, "test_small.csv"),
gene_names=True,
cell_names=[0] + list(range(X_csv.shape[1] - 1)),
)
X_csv2 = scprep.io.load_csv(
os.path.join(data.data_dir, "test_small.csv"),
gene_names=True,
cell_names=None,
index_col=0,
)
X_csv3 = scprep.io.load_csv(
os.path.join(data.data_dir, "test_small.csv"),
gene_names=None,
cell_names=True,
header=0,
)
X_csv4 = scprep.io.load_csv(
os.path.join(data.data_dir, "test_small.csv"),
gene_names=True,
cell_names=True,
cell_axis="col",
)
X_tsv = scprep.io.load_tsv(os.path.join(data.data_dir, "test_small.tsv"))
assert np.sum(np.sum(X != X_csv)) == 0
assert np.sum(np.sum(X_csv != X_csv2)) == 0
assert np.sum(np.sum(X_csv != X_csv3)) == 0
assert np.sum(np.sum(X_csv != X_csv4.T)) == 0
assert np.sum(np.sum(X_csv != X_tsv)) == 0
np.testing.assert_array_equal(X.columns, X_csv.columns)
np.testing.assert_array_equal(X.index, X_csv.index)
np.testing.assert_array_equal(X_csv.columns, X_csv2.columns)
np.testing.assert_array_equal(X_csv.index, X_csv2.index)
np.testing.assert_array_equal(X_csv.columns, X_csv3.columns)
np.testing.assert_array_equal(X_csv.index, X_csv3.index)
np.testing.assert_array_equal(X_csv.columns, X_csv4.index)
np.testing.assert_array_equal(X_csv.index, X_csv4.columns)
assert isinstance(X_csv, pd.DataFrame)
assert not scprep.utils.is_sparse_dataframe(X_csv)
X_csv = scprep.io.load_csv(
os.path.join(data.data_dir, "test_small.csv"),
gene_names=os.path.join(data.data_dir, "gene_symbols.csv"),
cell_names=os.path.join(data.data_dir, "barcodes.tsv"),
skiprows=1,
usecols=range(1, 101),
)
assert np.sum(np.sum(X != X_csv)) == 0
np.testing.assert_array_equal(X.columns, X_csv.columns)
np.testing.assert_array_equal(X.index, X_csv.index)
assert isinstance(X_csv, pd.DataFrame)
assert not scprep.utils.is_sparse_dataframe(X_csv)
X_csv = scprep.io.load_csv(
os.path.join(data.data_dir, "test_small.csv"),
gene_names=X.columns,
cell_names=X.index,
skiprows=1,
usecols=range(1, 101),
)
assert np.sum(np.sum(X != X_csv)) == 0
np.testing.assert_array_equal(X.columns, X_csv.columns)
np.testing.assert_array_equal(X.index, X_csv.index)
assert isinstance(X_csv, pd.DataFrame)
assert not scprep.utils.is_sparse_dataframe(X_csv)
X_csv = scprep.io.load_csv(
os.path.join(data.data_dir, "test_small.csv"),
gene_names=None,
cell_names=None,
sparse=True,
skiprows=1,
usecols=range(1, 101),
)
assert np.sum(np.sum(X.to_numpy() != X_csv.to_numpy())) == 0
assert scprep.utils.is_sparse_dataframe(X_csv)
X_csv = scprep.io.load_csv(
os.path.join(data.data_dir, "test_small_duplicate_gene_names.csv"))
assert "DUPLICATE" in X_csv.columns
assert "DUPLICATE.1" in X_csv.columns
utils.assert_raises_message(
ValueError,
"cell_axis neither not recognized. "
"Expected 'row' or 'column'",
scprep.io.load_csv,
filename,
cell_axis="neither",
)
