def calc_dist_mat(self, seqs, seqs2=None):
"""Don't calculate distances, but return the
hard-coded distance matrix needed for the test"""
mat_seqs = np.array(["AAA", "AHA", "KK", "KKK", "KKY", "LLL"])
mask = np.isin(mat_seqs, seqs)
dist_mat = scipy.sparse.csr_matrix(
np.array([
[1, 4, 0, 0, 0, 0],
[4, 1, 0, 0, 0, 0],
[0, 0, 1, 10, 10, 0],
[0, 0, 10, 1, 5, 0],
[0, 0, 10, 5, 1, 0],
[0, 0, 0, 0, 0, 1],
])[mask, :][:, mask])
assert _is_symmetric(dist_mat)
return dist_mat
def adata_conn():
"""Adata with connectivities computed"""
adata = AnnData(obs=pd.DataFrame().assign(
cell_id=["cell1", "cell2", "cell3", "cell4"],
TRA_1_v_gene=["av1", "av1", "av2", "av1"],
TRB_1_v_gene=["bv1", "bv1", "bv2", "bv1"],
TRA_2_v_gene=["a2v1", "a2v2", "a2v2", "a2v1"],
TRB_2_v_gene=["b2v1", "b2v2", "b2v2", "b2v1"],
).set_index("cell_id"))
adata.uns["tcr_neighbors_aa_alignment"] = {
"connectivities":
np.array([[1, 0, 0.5, 0], [0, 1, 1, 0], [0.5, 1, 1, 0], [0, 0, 0, 1]])
}
assert _is_symmetric(
adata.uns["tcr_neighbors_aa_alignment"]["connectivities"])
return adata
def adata_clonotype_network():
"""Adata with clonotype network computed"""
adata = AnnData(obs=pd.DataFrame().assign(
cell_id=["cell1", "cell2", "cell3", "cell4"]).set_index("cell_id"))
adata.uns["foo_neighbors"] = {
"connectivities":
np.array([[1, 0, 0.5, 0], [0, 1, 1, 0], [0.5, 1, 1, 0], [0, 0, 0, 1]])
}
adata.uns["clonotype_network"] = {"neighbors_key": "foo_neighbors"}
adata.obsm["X_clonotype_network"] = np.array([
[2.41359095, 0.23412465],
[np.nan, np.nan],
[1.61680611, 0.80266963],
[3.06104282, 2.14395562],
])
assert _is_symmetric(adata.uns["foo_neighbors"]["connectivities"])
return adata
def adata_conn():
"""Adata with connectivities computed"""
adata = AnnData(obs=pd.DataFrame().assign(
cell_id=["cell1", "cell2", "cell3", "cell4"],
IR_VJ_1_v_gene=["av1", "av1", "av2", "av1"],
IR_VDJ_1_v_gene=["bv1", "bv1", "bv2", "bv1"],
IR_VJ_2_v_gene=["a2v1", "a2v2", "a2v2", "a2v1"],
IR_VDJ_2_v_gene=["b2v1", "b2v2", "b2v2", "b2v1"],
IR_VJ_1_LOCUS=["TRA", "IGL", "IGL", "IGK"],
IR_VDJ_1_LOCUS=["TRB", "IGH", "IGH", "IGH"],
IR_VJ_2_LOCUS=["TRA", "IGL", "IGL", "IGK"],
IR_VDJ_2_LOCUS=["TRB", "IGH", "IGH", "IGH"],
receptor_type=["TCR", "BCR", "BCR", "BCR"],
).set_index("cell_id"))
adata.uns["ir_neighbors_aa_alignment"] = {
"connectivities":
csr_matrix([[1, 0, 0.5, 0], [0, 1, 1, 0], [0.5, 1, 1, 0], [0, 0, 0,
1]])
}
assert _is_symmetric(
adata.uns["ir_neighbors_aa_alignment"]["connectivities"])
return adata
def test_is_symmatric():
M = np.array([[1, 2, 2], [2, 1, 3], [2, 3, 1]])
S_csr = scipy.sparse.csr_matrix(M)
S_csc = scipy.sparse.csc_matrix(M)
S_lil = scipy.sparse.lil_matrix(M)
assert _is_symmetric(M)
assert _is_symmetric(S_csr)
assert _is_symmetric(S_csc)
assert _is_symmetric(S_lil)
M = np.array([[1, 2, 2], [2, 1, np.nan], [2, np.nan, np.nan]])
S_csr = scipy.sparse.csr_matrix(M)
S_csc = scipy.sparse.csc_matrix(M)
S_lil = scipy.sparse.lil_matrix(M)
assert _is_symmetric(M)
assert _is_symmetric(S_csr)
assert _is_symmetric(S_csc)
assert _is_symmetric(S_lil)
M = np.array([[1, 2, 2], [2, 1, 3], [3, 2, 1]])
S_csr = scipy.sparse.csr_matrix(M)
S_csc = scipy.sparse.csc_matrix(M)
S_lil = scipy.sparse.lil_matrix(M)
assert not _is_symmetric(M)
assert not _is_symmetric(S_csr)
assert not _is_symmetric(S_csc)
assert not _is_symmetric(S_lil)