def test_extract_edge_weights(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
x = ddl.tl.extract_edge_weights(vdj)
assert x is None
x = ddl.tl.extract_edge_weights(vdj, expanded_only=True)
assert x is None
def test_diversity_gini2(create_testfolder, metric):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
vdj.data['sample_id'] = 'sample_test'
vdj.data['contig_QC_pass'] = '******'
ddl.update_metadata(vdj,
retrieve=['sample_id', 'contig_QC_pass'],
split=False)
ddl.tl.clone_diversity(vdj,
groupby='sample_id',
resample=True,
downsample=6,
key='sequence',
n_resample=5,
metric=metric)
if metric == 'clone_network' or metric is None:
assert not vdj.metadata.clone_network_cluster_size_gini.empty
assert not vdj.metadata.clone_network_vertex_size_gini.empty
if metric == 'clone_degree':
assert not vdj.metadata.clone_degree.empty
assert not vdj.metadata.clone_size_gini.empty
assert not vdj.metadata.clone_degree_gini.empty
if metric == 'clone_centrality':
assert not vdj.metadata.clone_centrality.empty
assert not vdj.metadata.clone_centrality_gini.empty
def test_clone_size(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
ddl.tl.clone_size(vdj)
assert not vdj.metadata.clone_id_size.empty
ddl.tl.clone_size(vdj, max_size=3)
assert not vdj.metadata.clone_id_size.empty
def test_diversity_shannon(create_testfolder, resample, normalize):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
if resample:
ddl.tl.clone_diversity(vdj,
groupby='sample_id',
method='shannon',
resample=resample,
normalize=normalize,
downsample=6)
else:
ddl.tl.clone_diversity(vdj,
groupby='sample_id',
method='shannon',
resample=resample,
normalize=normalize)
if normalize:
assert not vdj.metadata.clone_size_normalized_shannon.empty
else:
assert not vdj.metadata.clone_size_shannon.empty
tmp = ddl.tl.clone_diversity(vdj,
groupby='sample_id',
method='shannon',
update_obs_meta=False)
assert isinstance(tmp, pd.DataFrame)
def test_plot_stackedbar(create_testfolder, norm):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
ax = ddl.pl.stackedbarplot(vdj,
color='v_call_genotyped_VDJ',
groupby='isotype',
normalize=norm)
assert ax is not None
def test_plot_spectratype(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
ax = ddl.pl.spectratype(vdj,
color='junction_length',
groupby='c_call',
locus='IGH')
assert ax is not None
def test_filter():
adata = sc.read_10x_h5("tests/sctest.h5")
test = ddl.read_h5("tests/test.h5")
adata.obs["filter_rna"] = False
test, adata = ddl.pp.filter_bcr(test, adata)
adata.write("tests/sctest.h5ad", compression="gzip")
test.write_h5("tests/test.h5", compression="bzip2")
print(test)
def test_find_clones(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
ddl.tl.find_clones(vdj)
assert not vdj.data.clone_id.empty
assert not vdj.metadata.clone_id.empty
assert len(set(x for x in vdj.metadata['clone_id'] if pd.notnull(x))) == 4
vdj.write_h5(f)
def test_diversity_gini(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
ddl.tl.clone_diversity(vdj, groupby='sample_id')
tmp = ddl.tl.clone_diversity(vdj,
groupby='sample_id',
update_obs_meta=False)
assert isinstance(tmp, pd.DataFrame)
def test_create_germlines():
test = ddl.read_h5("tests/test.h5")
test.update_germline(germline="database/germlines/imgt/human/vdj/")
ddl.pp.create_germlines(test,
germline="database/germlines/imgt/human/vdj/",
v_field="v_call",
germ_types="dmask")
test.write_h5("tests/test.h5", compression="bzip2")
print(test)
def test_generate_network(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
ddl.tl.find_clones(vdj)
ddl.tl.generate_network(vdj)
assert vdj.distance is not None
assert vdj.edges is not None
assert vdj.n_obs == 392
assert vdj.layout is not None
assert vdj.graph is not None
def test_diversity_gini(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
ddl.tl.clone_diversity(vdj, groupby='sample_id')
assert not vdj.metadata.clone_network_vertex_size_gini.empty
assert not vdj.metadata.clone_network_cluster_size_gini.empty
ddl.tl.generate_network(vdj)
ddl.tl.clone_diversity(vdj, groupby='sample_id', metric='clone_centrality')
assert not vdj.metadata.clone_centrality_gini.empty
assert not vdj.metadata.clone_size_gini.empty
def test_transfer(create_testfolder, dummy_adata):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
vdj, adata = ddl.pp.filter_contigs(vdj, dummy_adata)
ddl.tl.transfer(dummy_adata, vdj)
assert 'clone_id' in dummy_adata.obs
ddl.tl.generate_network(vdj)
ddl.tl.transfer(dummy_adata, vdj)
assert 'X_vdj' in dummy_adata.obsm
f2 = create_testfolder / "test.h5ad"
dummy_adata.write_h5ad(f2)
def test_setup(create_testfolder, airr_reannotated, dummy_adata):
vdj, adata = ddl.pp.filter_contigs(airr_reannotated, dummy_adata)
assert airr_reannotated.shape[0] == 9
assert vdj.data.shape[0] == 7
assert vdj.metadata.shape[0] == 4
assert adata.n_obs == 5
f = create_testfolder / "test.h5"
vdj.write_h5(f)
assert len(list(create_testfolder.iterdir())) == 1
vdj2 = ddl.read_h5(f)
assert vdj2.metadata is not None
def test_find_clones_key(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
ddl.tl.find_clones(vdj, key_added='test_clone')
assert not vdj.metadata.test_clone.empty
assert vdj.data.test_clone.dtype == 'object'
ddl.tl.generate_network(vdj, clone_key='test_clone')
assert vdj.distance is not None
assert vdj.edges is None
assert vdj.layout is not None
assert vdj.graph is not None
def test_plot_bar(create_testfolder, sort, norm):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
ax = ddl.pl.barplot(vdj, color='v_call_genotyped_VDJ')
assert ax is not None
ax = ddl.pl.barplot(vdj,
color='v_call_genotyped_VDJ',
sort_descending=sort)
assert ax is not None
ax = ddl.pl.barplot(vdj, color='v_call_genotyped_VDJ', normalize=norm)
assert ax is not None
def test_diversity_rarefaction3(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
vdj.data['sample_id'] = 'sample_test'
vdj.data['contig_QC_pass'] = '******'
ddl.update_metadata(vdj,
retrieve=['sample_id', 'contig_QC_pass'],
split=False)
df = ddl.tl.clone_rarefaction(vdj, groupby='sample_id')
assert isinstance(df, dict)
p = ddl.pl.clone_rarefaction(vdj, color='sample_id')
assert p is not None
def test_diversity2c(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
vdj.data['sample_id'] = 'sample_test'
vdj.data['contig_QC_pass'] = '******'
ddl.update_metadata(vdj,
retrieve=['sample_id', 'contig_QC_pass'],
split=False)
x = ddl.tl.clone_diversity(vdj,
groupby='sample_id',
key='sequence',
update_obs_meta=False)
assert isinstance(x, pd.DataFrame)
def test_diversity2b(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
vdj.data['sample_id'] = 'sample_test'
vdj.data['contig_QC_pass'] = '******'
ddl.update_metadata(vdj,
retrieve=['sample_id', 'contig_QC_pass'],
split=False)
ddl.tl.clone_diversity(vdj,
groupby='sample_id',
use_contracted=True,
key='sequence')
assert not vdj.metadata.clone_network_cluster_size_gini.empty
assert not vdj.metadata.clone_network_vertex_size_gini.empty
def test_diversity2a(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
vdj.data['sample_id'] = 'sample_test'
vdj.data['contig_QC_pass'] = '******'
ddl.update_metadata(
vdj,
retrieve=['sample_id', 'contig_QC_pass'],
retrieve_mode=['merge and unique only', 'merge and unique only'])
ddl.tl.clone_diversity(vdj,
groupby='sample_id',
reconstruct_network=False,
key='sequence')
assert not vdj.metadata.clone_network_cluster_size_gini.empty
assert not vdj.metadata.clone_network_vertex_size_gini.empty
def test_diversity_gini(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
ddl.tl.clone_diversity(vdj, groupby='sample_id')
assert not vdj.metadata.clone_network_vertex_size_gini.empty
assert not vdj.metadata.clone_network_cluster_size_gini.empty
ddl.tl.generate_network(vdj)
ddl.tl.clone_diversity(vdj, groupby='sample_id', metric='clone_centrality')
assert not vdj.metadata.clone_centrality_gini.empty
assert not vdj.metadata.clone_size_gini.empty
tmp = ddl.tl.clone_diversity(vdj,
groupby='sample_id',
metric='clone_centrality',
update_obs_meta=False)
assert isinstance(tmp, pd.DataFrame)
def test_diversity_chao(create_testfolder, resample):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
if resample:
ddl.tl.clone_diversity(vdj,
groupby='sample_id',
method='chao1',
resample=resample,
downsample=6)
else:
ddl.tl.clone_diversity(vdj,
groupby='sample_id',
method='chao1',
resample=resample)
assert not vdj.metadata.clone_size_chao1.empty
def test_readwrite_h5(create_testfolder):
out_file1 = str(create_testfolder) + "/test_airr_reannotated.tsv"
out_file2 = str(create_testfolder) + "/test_airr_reannotated.h5"
vdj = ddl.Dandelion(out_file1)
assert not vdj.data.np1_length.empty
assert not vdj.data.np2_length.empty
assert not vdj.data.junction_length.empty
vdj.write_h5(out_file2)
vdj2 = ddl.read_h5(out_file2)
assert not vdj2.data.np1_length.empty
assert not vdj2.data.np2_length.empty
assert not vdj2.data.junction_length.empty
vdj.write_h5(out_file2, complib='blosc:lz4')
vdj2 = ddl.read_h5(out_file2)
assert not vdj2.data.np1_length.empty
assert not vdj2.data.np2_length.empty
assert not vdj2.data.junction_length.empty
vdj.write_h5(out_file2, compression='blosc:lz4')
vdj2 = ddl.read_h5(out_file2)
assert not vdj2.data.np1_length.empty
assert not vdj2.data.np2_length.empty
assert not vdj2.data.junction_length.empty
with pytest.raises(ValueError):
vdj.write_h5(out_file2, complib='blosc:lz4', compression='blosc:lz4')
def test_generate_network(create_testfolder, resample, expected):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
if resample is not None:
vdj = ddl.tl.generate_network(vdj, downsample=resample)
else:
ddl.tl.generate_network(vdj)
assert vdj.distance is not None
assert vdj.edges is None
assert vdj.n_obs == expected
assert vdj.layout is not None
assert vdj.graph is not None
vdj.data['clone_id'] = '1'
vdj = ddl.Dandelion(vdj.data)
assert vdj.data.clone_id.dtype == 'object'
ddl.tl.generate_network(vdj)
assert vdj.edges is not None
def test_transfer():
test = ddl.read_h5("tests/test2.h5")
adata = sc.read_h5ad("tests/sctest2.h5ad")
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata,
min_mean=0.0125,
max_mean=3,
min_disp=0.5)
adata = adata[:, adata.var["highly_variable"]].copy()
sc.pp.scale(adata, max_value=10)
sc.tl.pca(adata, svd_solver="arpack")
sc.pp.neighbors(adata)
ddl.tl.transfer(adata, test)
adata.write("tests/sctest2.h5ad", compression="gzip")
print(adata)
def test_update_germlines(create_testfolder, processed_files, database_paths,
fasta_10x):
f = create_testfolder / str('dandelion/' + processed_files['filtered'])
vdj = ddl.Dandelion(f)
vdj.update_germline(germline=database_paths['germline'])
assert len(vdj.germline) > 0
out_file = str(create_testfolder) + "/test_airr_reannotated.h5"
vdj.write_h5(out_file)
tmp = ddl.read_h5(out_file)
assert len(tmp.germline) > 0
vdj.update_germline(germline=database_paths['germline'],
corrected=str(create_testfolder) +
"/filtered_contig.fasta")
assert len(vdj.germline) > 0
vdj.update_germline(germline=database_paths['germline'],
corrected=fasta_10x)
assert len(vdj.germline) > 0
with pytest.raises(TypeError):
vdj.update_germline(germline=database_paths['germline'], corrected=[])
def test_diversity_chao(create_testfolder, resample):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
if resample:
ddl.tl.clone_diversity(vdj,
groupby='sample_id',
method='chao1',
resample=resample,
downsample=6)
else:
ddl.tl.clone_diversity(vdj,
groupby='sample_id',
method='chao1',
resample=resample)
assert not vdj.metadata.clone_size_chao1.empty
tmp = ddl.tl.clone_diversity(vdj,
groupby='sample_id',
method='chao1',
update_obs_meta=False)
assert isinstance(tmp, pd.DataFrame)
def test_setup():
file = "https://cf.10xgenomics.com/samples/cell-vdj/5.0.0/sc5p_v2_hs_B_1k_multi_5gex_b/sc5p_v2_hs_B_1k_multi_5gex_b_vdj_b_airr_rearrangement.tsv"
r = requests.get(file)
test_data = pd.read_csv(StringIO(r.text), sep="\t")
test_data["locus"] = [
"IGH" if "IGH" in i else
"IGK" if "IGK" in i else "IGL" if "IGL" in i else None
for i in test_data.v_call
]
test_data["umi_count"] = test_data["duplicate_count"]
test_data["sample_id"] = "test"
test_ddl = ddl.Dandelion(test_data)
test_ddl.write_h5("tests/test.h5", compression="bzip2")
test_ddl.write_pkl("tests/test.pkl.pbz2")
test = ddl.read_h5("tests/test.h5")
_ = ddl.read_pkl("tests/test.pkl.pbz2")
scfile = "https://cf.10xgenomics.com/samples/cell-vdj/5.0.0/sc5p_v2_hs_B_1k_multi_5gex_b/sc5p_v2_hs_B_1k_multi_5gex_b_count_filtered_feature_bc_matrix.h5"
r = requests.get(scfile)
open("tests/sctest.h5", "wb").write(r.content)
adata = sc.read_10x_h5("tests/sctest.h5")
adata.write("tests/sctest.h5ad", compression="gzip")
print(test)
print(adata)
def test_diversity_gini(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
ddl.tl.clone_diversity(vdj, groupby='sample_id')
def test_diversity_gini_simple(create_testfolder):
f = create_testfolder / "test.h5"
vdj = ddl.read_h5(f)
