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test_get.py
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from functools import partial
from itertools import repeat, chain
import numpy as np
import pandas as pd
import pytest
from anndata import AnnData
from scipy import sparse
import scanpy as sc
from scanpy.datasets._utils import filter_oldformatwarning
from scanpy.testing._helpers.data import pbmc68k_reduced
TRANSPOSE_PARAMS = pytest.mark.parametrize(
"dim,transform,func",
[
(
"obs",
lambda x: x,
sc.get.obs_df,
),
(
"var",
lambda x: x.T,
sc.get.var_df,
),
],
ids=["obs_df", "var_df"],
)
@pytest.fixture
def adata():
"""
adata.X is np.ones((2, 2))
adata.layers['double'] is sparse np.ones((2,2)) * 2 to also test sparse matrices
"""
return AnnData(
X=np.ones((2, 2), dtype=int),
obs=pd.DataFrame(
{"obs1": [0, 1], "obs2": ["a", "b"]}, index=["cell1", "cell2"]
),
var=pd.DataFrame(
{"gene_symbols": ["genesymbol1", "genesymbol2"]}, index=["gene1", "gene2"]
),
layers={"double": sparse.csr_matrix(np.ones((2, 2)), dtype=int) * 2},
)
########################
# obs_df, var_df tests #
########################
def test_obs_df(adata):
adata.obsm["eye"] = np.eye(2, dtype=int)
adata.obsm["sparse"] = sparse.csr_matrix(np.eye(2), dtype='float64')
# make raw with different genes than adata
adata.raw = AnnData(
X=np.array([[1, 2, 3], [2, 4, 6]], dtype=np.float64),
var=pd.DataFrame(
{"gene_symbols": ["raw1", "raw2", 'raw3']},
index=["gene2", "gene3", "gene4"],
),
)
pd.testing.assert_frame_equal(
sc.get.obs_df(
adata, keys=["gene2", "obs1"], obsm_keys=[("eye", 0), ("sparse", 1)]
),
pd.DataFrame(
{"gene2": [1, 1], "obs1": [0, 1], "eye-0": [1, 0], "sparse-1": [0.0, 1.0]},
index=adata.obs_names,
),
)
pd.testing.assert_frame_equal(
sc.get.obs_df(
adata,
keys=["genesymbol2", "obs1"],
obsm_keys=[("eye", 0), ("sparse", 1)],
gene_symbols="gene_symbols",
),
pd.DataFrame(
{
"genesymbol2": [1, 1],
"obs1": [0, 1],
"eye-0": [1, 0],
"sparse-1": [0.0, 1.0],
},
index=adata.obs_names,
),
)
pd.testing.assert_frame_equal(
sc.get.obs_df(adata, keys=["gene2", "obs1"], layer="double"),
pd.DataFrame({"gene2": [2, 2], "obs1": [0, 1]}, index=adata.obs_names),
)
pd.testing.assert_frame_equal(
sc.get.obs_df(
adata,
keys=["raw2", "raw3", "obs1"],
gene_symbols="gene_symbols",
use_raw=True,
),
pd.DataFrame(
{"raw2": [2.0, 4.0], "raw3": [3.0, 6.0], "obs1": [0, 1]},
index=adata.obs_names,
),
)
# test only obs
pd.testing.assert_frame_equal(
sc.get.obs_df(adata, keys=["obs1", "obs2"]),
pd.DataFrame({"obs1": [0, 1], "obs2": ["a", "b"]}, index=["cell1", "cell2"]),
)
# test only var
pd.testing.assert_frame_equal(
sc.get.obs_df(adata, keys=["gene1", "gene2"]),
pd.DataFrame({"gene1": [1, 1], "gene2": [1, 1]}, index=adata.obs_names),
)
pd.testing.assert_frame_equal(
sc.get.obs_df(adata, keys=["gene1", "gene2"]),
pd.DataFrame({"gene1": [1, 1], "gene2": [1, 1]}, index=adata.obs_names),
)
# test handling of duplicated keys (in this case repeated gene names)
pd.testing.assert_frame_equal(
sc.get.obs_df(adata, keys=["gene1", "gene2", "gene1", "gene1"]),
pd.DataFrame(
{"gene1": [1, 1], "gene2": [1, 1]},
index=adata.obs_names,
)[["gene1", "gene2", "gene1", "gene1"]],
)
badkeys = ["badkey1", "badkey2"]
with pytest.raises(KeyError) as badkey_err:
sc.get.obs_df(adata, keys=badkeys)
with pytest.raises(AssertionError):
sc.get.obs_df(adata, keys=["gene1"], use_raw=True, layer="double")
assert all(badkey_err.match(k) for k in badkeys)
# test non unique index
adata = sc.AnnData(
np.arange(16).reshape(4, 4),
obs=pd.DataFrame(index=["a", "a", "b", "c"]),
var=pd.DataFrame(index=[f"gene{i}" for i in range(4)]),
)
df = sc.get.obs_df(adata, ["gene1"])
pd.testing.assert_index_equal(df.index, adata.obs_names)
def test_repeated_gene_symbols():
"""
Gene symbols column allows repeats, but we can't unambiguously get data for these values.
"""
gene_symbols = [f"symbol_{i}" for i in ["a", "b", "b", "c"]]
var_names = pd.Index([f"id_{i}" for i in ["a", "b.1", "b.2", "c"]])
adata = sc.AnnData(
np.arange(3 * 4, dtype=np.float32).reshape((3, 4)),
var=pd.DataFrame({"gene_symbols": gene_symbols}, index=var_names),
)
with pytest.raises(KeyError, match="symbol_b"):
sc.get.obs_df(adata, ["symbol_b"], gene_symbols="gene_symbols")
expected = pd.DataFrame(
np.arange(3 * 4).reshape((3, 4))[:, [0, 3]].astype(np.float32),
index=adata.obs_names,
columns=["symbol_a", "symbol_c"],
)
result = sc.get.obs_df(adata, ["symbol_a", "symbol_c"], gene_symbols="gene_symbols")
pd.testing.assert_frame_equal(expected, result)
@filter_oldformatwarning
def test_backed_vs_memory():
"""compares backed vs. memory"""
from pathlib import Path
# get location test h5ad file in datasets
HERE = Path(sc.__file__).parent
adata_file = HERE / "datasets/10x_pbmc68k_reduced.h5ad"
adata_backed = sc.read(adata_file, backed='r')
adata = sc.read_h5ad(adata_file)
# use non-sequential list of genes
genes = list(adata.var_names[20::-2])
obs_names = ['bulk_labels', 'n_genes']
pd.testing.assert_frame_equal(
sc.get.obs_df(adata, keys=genes + obs_names),
sc.get.obs_df(adata_backed, keys=genes + obs_names),
)
# use non-sequential list of cell indices
cell_indices = list(adata.obs_names[30::-2])
pd.testing.assert_frame_equal(
sc.get.var_df(adata, keys=cell_indices + ["highly_variable"]),
sc.get.var_df(adata_backed, keys=cell_indices + ["highly_variable"]),
)
def test_column_content():
"""uses a larger dataset to test column order and content"""
adata = pbmc68k_reduced()
# test that columns content is correct for obs_df
query = ['CST3', 'NKG7', 'GNLY', 'louvain', 'n_counts', 'n_genes']
df = sc.get.obs_df(adata, query)
for col in query:
assert col in df
np.testing.assert_array_equal(query, df.columns)
np.testing.assert_array_equal(df[col].values, adata.obs_vector(col))
# test that columns content is correct for var_df
cell_ids = list(adata.obs.sample(5).index)
query = cell_ids + ['highly_variable', 'dispersions_norm', 'dispersions']
df = sc.get.var_df(adata, query)
np.testing.assert_array_equal(query, df.columns)
for col in query:
np.testing.assert_array_equal(df[col].values, adata.var_vector(col))
def test_var_df(adata):
adata.varm["eye"] = np.eye(2, dtype=int)
adata.varm["sparse"] = sparse.csr_matrix(np.eye(2), dtype='float64')
pd.testing.assert_frame_equal(
sc.get.var_df(
adata,
keys=["cell2", "gene_symbols"],
varm_keys=[("eye", 0), ("sparse", 1)],
),
pd.DataFrame(
{
"cell2": [1, 1],
"gene_symbols": ["genesymbol1", "genesymbol2"],
"eye-0": [1, 0],
"sparse-1": [0.0, 1.0],
},
index=adata.var_names,
),
)
pd.testing.assert_frame_equal(
sc.get.var_df(adata, keys=["cell1", "gene_symbols"], layer="double"),
pd.DataFrame(
{"cell1": [2, 2], "gene_symbols": ["genesymbol1", "genesymbol2"]},
index=adata.var_names,
),
)
# test only cells
pd.testing.assert_frame_equal(
sc.get.var_df(adata, keys=["cell1", "cell2"]),
pd.DataFrame(
{"cell1": [1, 1], "cell2": [1, 1]},
index=adata.var_names,
),
)
# test only var columns
pd.testing.assert_frame_equal(
sc.get.var_df(adata, keys=["gene_symbols"]),
pd.DataFrame(
{"gene_symbols": ["genesymbol1", "genesymbol2"]},
index=adata.var_names,
),
)
# test handling of duplicated keys (in this case repeated cell names)
pd.testing.assert_frame_equal(
sc.get.var_df(adata, keys=["cell1", "cell2", "cell2", "cell1"]),
pd.DataFrame(
{"cell1": [1, 1], "cell2": [1, 1]},
index=adata.var_names,
)[["cell1", "cell2", "cell2", "cell1"]],
)
badkeys = ["badkey1", "badkey2"]
with pytest.raises(KeyError) as badkey_err:
sc.get.var_df(adata, keys=badkeys)
assert all(badkey_err.match(k) for k in badkeys)
@TRANSPOSE_PARAMS
def test_just_mapping_keys(dim, transform, func):
# https://github.com/scverse/scanpy/issues/1634
# Test for error where just passing obsm_keys, but not keys, would cause error.
mapping_attr = f"{dim}m"
kwargs = {f"{mapping_attr}_keys": [("array", 0), ("array", 1)]}
adata = transform(
sc.AnnData(
X=np.zeros((5, 5)),
obsm={
"array": np.arange(10).reshape((5, 2)),
},
)
)
expected = pd.DataFrame(
np.arange(10).reshape((5, 2)),
index=getattr(adata, f"{dim}_names"),
columns=["array-0", "array-1"],
)
result = func(adata, **kwargs)
pd.testing.assert_frame_equal(expected, result)
##################################
# Test errors for obs_df, var_df #
##################################
def test_non_unique_cols_value_error():
M, N = 5, 3
adata = sc.AnnData(
X=np.zeros((M, N)),
obs=pd.DataFrame(
np.arange(M * 2).reshape((M, 2)),
columns=["repeated_col", "repeated_col"],
index=[f"cell_{i}" for i in range(M)],
),
var=pd.DataFrame(
index=[f"gene_{i}" for i in range(N)],
),
)
with pytest.raises(ValueError):
sc.get.obs_df(adata, ["repeated_col"])
def test_non_unique_var_index_value_error():
adata = sc.AnnData(
X=np.ones((2, 3)),
obs=pd.DataFrame(index=["cell-0", "cell-1"]),
var=pd.DataFrame(index=["gene-0", "gene-0", "gene-1"]),
)
with pytest.raises(ValueError):
sc.get.obs_df(adata, ["gene-0"])
def test_keys_in_both_obs_and_var_index_value_error():
M, N = 5, 3
adata = sc.AnnData(
X=np.zeros((M, N)),
obs=pd.DataFrame(
np.arange(M),
columns=["var_id"],
index=[f"cell_{i}" for i in range(M)],
),
var=pd.DataFrame(
index=["var_id"] + [f"gene_{i}" for i in range(N - 1)],
),
)
with pytest.raises(KeyError, match="var_id"):
sc.get.obs_df(adata, ["var_id"])
@TRANSPOSE_PARAMS
def test_repeated_cols(dim, transform, func):
adata = transform(
sc.AnnData(
np.ones((5, 10)),
obs=pd.DataFrame(
np.ones((5, 2)), columns=["a_column_name", "a_column_name"]
),
var=pd.DataFrame(index=[f"gene-{i}" for i in range(10)]),
)
)
# (?s) is inline re.DOTALL
with pytest.raises(ValueError, match=rf"(?s)^adata\.{dim}.*a_column_name.*$"):
func(adata, ["gene_5"])
@TRANSPOSE_PARAMS
def test_repeated_index_vals(dim, transform, func):
# THis one could be reverted, see:
# https://github.com/scverse/scanpy/pull/1583#issuecomment-770641710
alt_dim = ["obs", "var"][dim == "obs"]
adata = transform(
sc.AnnData(
np.ones((5, 10)),
var=pd.DataFrame(
index=["repeated_id"] * 2 + [f"gene-{i}" for i in range(8)]
),
)
)
with pytest.raises(
ValueError,
match=rf"(?s)adata\.{alt_dim}_names.*{alt_dim}_names_make_unique",
):
func(adata, "gene_5")
@pytest.fixture(
params=[
"obs_df",
"var_df",
"obs_df:use_raw",
"obs_df:gene_symbols",
"obs_df:gene_symbols,use_raw",
]
)
def shared_key_adata(request):
kind = request.param
adata = sc.AnnData(
np.arange(50).reshape((5, 10)),
obs=pd.DataFrame(np.zeros((5, 1)), columns=["var_id"]),
var=pd.DataFrame(index=["var_id"] + [f"gene_{i}" for i in range(1, 10)]),
)
if kind == "obs_df":
return (
adata,
sc.get.obs_df,
r"'var_id'.* adata\.obs .* adata.var_names",
)
elif kind == "var_df":
return (
adata.T,
sc.get.var_df,
r"'var_id'.* adata\.var .* adata.obs_names",
)
elif kind == "obs_df:use_raw":
adata.raw = adata
adata.var_names = [f"gene_{i}" for i in range(10)]
return (
adata,
partial(sc.get.obs_df, use_raw=True),
r"'var_id'.* adata\.obs .* adata\.raw\.var_names",
)
elif kind == "obs_df:gene_symbols":
adata.var["gene_symbols"] = adata.var_names
adata.var_names = [f"gene_{i}" for i in range(10)]
return (
adata,
partial(sc.get.obs_df, gene_symbols="gene_symbols"),
r"'var_id'.* adata\.obs .* adata\.var\['gene_symbols'\]",
)
elif kind == "obs_df:gene_symbols,use_raw":
base = adata.copy()
adata.var["gene_symbols"] = adata.var_names
adata.var_names = [f"gene_{i}" for i in range(10)]
base.raw = adata
return (
base,
partial(
sc.get.obs_df,
gene_symbols="gene_symbols",
use_raw=True,
),
r"'var_id'.* adata\.obs .* adata\.raw\.var\['gene_symbols'\]",
)
else:
assert False
def test_shared_key_errors(shared_key_adata):
adata, func, regex = shared_key_adata
# This should error
with pytest.raises(KeyError, match=regex):
func(adata, keys=["var_id"])
# This shouldn't error
_ = func(adata, keys=["gene_2"])
##############################
# rank_genes_groups_df tests #
##############################
def test_rank_genes_groups_df():
a = np.zeros((20, 3))
a[:10, 0] = 5
adata = AnnData(
a,
obs=pd.DataFrame(
{"celltype": list(chain(repeat("a", 10), repeat("b", 10)))},
index=[f"cell{i}" for i in range(a.shape[0])],
),
var=pd.DataFrame(index=[f"gene{i}" for i in range(a.shape[1])]),
)
sc.tl.rank_genes_groups(adata, groupby="celltype", method="wilcoxon", pts=True)
dedf = sc.get.rank_genes_groups_df(adata, "a")
assert dedf["pvals"].value_counts()[1.0] == 2
assert sc.get.rank_genes_groups_df(adata, "a", log2fc_max=0.1).shape[0] == 2
assert sc.get.rank_genes_groups_df(adata, "a", log2fc_min=0.1).shape[0] == 1
assert sc.get.rank_genes_groups_df(adata, "a", pval_cutoff=0.9).shape[0] == 1
del adata.uns["rank_genes_groups"]
sc.tl.rank_genes_groups(
adata,
groupby="celltype",
method="wilcoxon",
key_added="different_key",
pts=True,
)
with pytest.raises(KeyError):
sc.get.rank_genes_groups_df(adata, "a")
dedf2 = sc.get.rank_genes_groups_df(adata, "a", key="different_key")
pd.testing.assert_frame_equal(dedf, dedf2)
assert 'pct_nz_group' in dedf2.columns
assert 'pct_nz_reference' in dedf2.columns
# get all groups
dedf3 = sc.get.rank_genes_groups_df(adata, group=None, key="different_key")
assert 'a' in dedf3['group'].unique()
assert 'b' in dedf3['group'].unique()
adata.var_names.name = 'pr1388'
sc.get.rank_genes_groups_df(adata, group=None, key="different_key")