def test_initialization_of_TCRsubset_alpha_beta_case_plus_motif_finding():
"""
Test that we can create a TCRsubset from paired input files
"""
import pytest
import pandas as pd
from tcrdist.subset import TCRsubset
from tcrdist.tests.my_test_subset import dist_a_subset, dist_b_subset, clone_df_subset
from tcrdist.cdr3_motif import TCRMotif
assert isinstance(dist_a_subset, pd.DataFrame)
assert isinstance(dist_b_subset, pd.DataFrame)
assert isinstance(clone_df_subset, pd.DataFrame)
df = clone_df_subset.iloc[0:20, :].copy()
db = dist_b_subset.iloc[0:20, 0:20]
da = dist_a_subset.iloc[0:20, 0:20]
ts = TCRsubset(clone_df=df,
organism="mouse",
epitopes=["PA"],
epitope="PA",
chains=["A", "B"],
dist_a=da,
dist_b=db)
motif_df = ts.find_motif()
assert isinstance(motif_df, pd.DataFrame)
assert isinstance(ts.motif_df, pd.DataFrame)
def test_initialization_of_TCRsubset_beta_only_find_motifs():
"""
Test that we can create a TCRsubset from paired input files
"""
assert isinstance(dist_a_subset, pd.DataFrame)
assert isinstance(dist_b_subset, pd.DataFrame)
assert isinstance(clone_df_subset, pd.DataFrame)
df = clone_df_subset[[
'clone_id', 'subject', 'epitope', 'v_b_gene', 'j_b_gene', 'cdr3_b_aa',
'cdr1_b_aa', 'cdr2_b_aa', 'pmhc_b_aa', 'cdr3_b_nucseq', 'count',
'vb_countreps', 'jb_countreps', 'vb_gene', 'jb_gene'
]]
df = df.iloc[0:20, :]
db = dist_b_subset.iloc[0:20, 0:20]
ts = TCRsubset(clone_df=df,
organism="mouse",
epitopes=["PA"],
epitope="PA",
chains=["beta"],
dist_b=db)
motif_df = ts.find_motif()
assert isinstance(motif_df, pd.DataFrame)
assert isinstance(ts.motif_df, pd.DataFrame)
def test_non_default_manual_generation_of_ng_tcrsAB():
""" Notices the usage of non default mode and specification of files"""
df = clone_df_subset.iloc[0:20, :].copy()
db = dist_b_subset.iloc[0:20, 0:20]
da = dist_a_subset.iloc[0:20, 0:20]
ts = TCRsubset(clone_df=df,
organism="mouse",
epitopes=["PA"],
epitope="PA",
chains=["A", "B"],
dist_a=da,
dist_b=db,
default_mode=False)
ts.ng_tcrs['B'] = ts.generate_background_set(
chain=['B'],
ng_log_path='tcrdist/db/alphabeta_db.tsv_files',
ng_log_file='new_nextgen_chains_mouse_B.tsv')
ts.ng_tcrs['A'] = ts.generate_background_set(
chain=['A'],
ng_log_path='tcrdist/db/alphabeta_db.tsv_files',
ng_log_file='new_nextgen_chains_mouse_A.tsv')
motif_df = ts.find_motif()
assert isinstance(motif_df, pd.DataFrame)
assert isinstance(ts.motif_df, pd.DataFrame)
assert motif_df.shape[0] > 1
def test_hot_start_example_in_full():
"""
This is the code that makes up the HotStart example in the docs
"""
# basic imports
import os
import pandas as pd
import numpy as np
#import IPython
# tcrdist classes
from tcrdist.repertoire import TCRrep
from tcrdist.subset import TCRsubset
from tcrdist.cdr3_motif import TCRMotif
from tcrdist.storage import StoreIOMotif, StoreIOEntropy
# tcrdist functions
from tcrdist import plotting
from tcrdist.mappers import populate_legacy_fields
# scipy functions for clustering
from scipy.spatial import distance
from scipy.cluster.hierarchy import linkage, dendrogram, fcluster
# sklearn functions for low-dimensional embeddings
from sklearn.manifold import TSNE, MDS
# plotnine to allow grammar of graphics plotting akin to R's ggplot2
#import plotnine as gg
#1 load data, subset to receptors recognizing "PA" epitope
tcrdist2_df = pd.read_csv(
os.path.join("tcrdist", "test_files_compact", "dash.csv"))
tcrdist2_df = tcrdist2_df[tcrdist2_df.epitope == "PA"].copy()
#2 create instance of TCRrep class, initializes input as tr.cell_df attribute
tr = TCRrep(cell_df=tcrdist2_df,
chains=['alpha', 'beta'],
organism="mouse")
#3 Infer CDR1,CDR2,CDR2.5 (a.k.a. phmc) from germline v-genes
tr.infer_cdrs_from_v_gene(chain='alpha', imgt_aligned=True)
tr.infer_cdrs_from_v_gene(chain='beta', imgt_aligned=True)
#4 Define index columns for determining unique clones.
tr.index_cols = [
'clone_id', 'subject', 'epitope', 'v_a_gene', 'j_a_gene', 'v_b_gene',
'j_b_gene', 'cdr3_a_aa', 'cdr3_b_aa', 'cdr1_a_aa', 'cdr2_a_aa',
'pmhc_a_aa', 'cdr1_b_aa', 'cdr2_b_aa', 'pmhc_b_aa', 'cdr3_b_nucseq',
'cdr3_a_nucseq'
]
#4 Deduplicate based on index cols, creating tr.clone_df attribute
tr.deduplicate()
#5 calculate tcrdists by method in Dash et al.
tr._tcrdist_legacy_method_alpha_beta()
#6 Check that sum of alpah-chain and beta-chain distance matrices equal paired_tcrdist
distA = tr.dist_a
distB = tr.dist_b
assert np.all(((distA + distB) - tr.paired_tcrdist) == 0)
# Cluster
from scipy.spatial import distance
from scipy.cluster.hierarchy import linkage, dendrogram, fcluster
compressed_dmat = distance.squareform(tr.paired_tcrdist, force="vector")
Z = linkage(compressed_dmat, method="complete")
den = dendrogram(Z, color_threshold=np.inf, no_plot=True)
cluster_index = fcluster(Z, t=20, criterion="maxclust")
assert len(cluster_index) == tr.clone_df.shape[0]
assert len(cluster_index) == tr.paired_tcrdist.shape[0]
tr.clone_df['cluster_index'] = cluster_index
# Subset to Cluster 5
criteria = (cluster_index == 5)
clone_df_subset = tr.clone_df[criteria]
clone_df_subset = clone_df_subset[clone_df_subset.epitope == "PA"].copy()
dist_a_subset = tr.dist_a.loc[clone_df_subset.clone_id,
clone_df_subset.clone_id].copy()
dist_b_subset = tr.dist_b.loc[clone_df_subset.clone_id,
clone_df_subset.clone_id].copy()
clone_df_subset = populate_legacy_fields(df=clone_df_subset,
chains=['alpha', 'beta'])
ts = TCRsubset(clone_df_subset,
organism="mouse",
epitopes=["PA"],
epitope="PA",
chains=["A", "B"],
dist_a=dist_a_subset,
dist_b=dist_b_subset)
# Find Motifs
if os.path.isfile(
os.path.join("tcrdist", "test_files_compact",
"dash_PA_cluster_5_motifs.csv")):
ts.motif_df = pd.read_csv(
os.path.join("tcrdist", "test_files_compact",
"dash_PA_cluster_5_motifs.csv"))
else:
motif_df = ts.find_motif()
# Save Motifs
ts.motif_df.to_csv(os.path.join("tcrdist", "test_files_compact",
"dash_PA_cluster_5_motifs.csv"),
index=False)
# Preprocess Motifs
motif_list_a = list()
motif_logos_a = list()
for i, row in ts.motif_df[ts.motif_df.ab == "A"].iterrows():
StoreIOMotif_instance = ts.eval_motif(row)
motif_list_a.append(StoreIOMotif_instance)
motif_logos_a.append(
plotting.plot_pwm(StoreIOMotif_instance,
create_file=False,
my_height=200,
my_width=600))
motif_list_b = list()
motif_logos_b = list()
for i, row in ts.motif_df[ts.motif_df.ab == "B"].iterrows():
StoreIOMotif_instance = ts.eval_motif(row)
motif_list_b.append(StoreIOMotif_instance)
motif_logos_b.append(
plotting.plot_pwm(StoreIOMotif_instance,
create_file=False,
my_height=200,
my_width=600))