def test_CompleteExample_with_TCRMotif_Invoked_From_within_TCRsubset():
import pandas as pd
import numpy as np
import tcrdist as td
#import IPython
from tcrdist import mappers
from tcrdist.repertoire import TCRrep
from tcrdist.cdr3_motif import TCRMotif
from tcrdist.subset import TCRsubset
from tcrdist.storage import StoreIOMotif, StoreIOEntropy
from tcrdist.plotting import plot_pwm
tcrdist_clone_fn = 'tcrdist/test_files/mouse_pairseqs_v1_parsed_seqs_probs_mq20_clones.tsv'
tcrdist_clone_df = pd.read_csv(tcrdist_clone_fn, sep="\t") #1
ind = (tcrdist_clone_df.epitope == "PA") | (tcrdist_clone_df.epitope
== "F2")
tcrdist_clone_df = tcrdist_clone_df[ind].copy()
mapping = mappers.tcrdist_clone_df_to_tcrdist2_mapping #3
tcrdist2_df = mappers.generic_pandas_mapper(
df=tcrdist_clone_df, #4
mapping=mapping)
#1
tr = TCRrep(cell_df=tcrdist2_df, organism="mouse")
#2
tr.infer_cdrs_from_v_gene(chain='alpha', imgt_aligned=True)
tr.infer_cdrs_from_v_gene(chain='beta', imgt_aligned=True)
#3
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', 'va_countreps', 'ja_countreps', 'vb_countreps',
'jb_countreps', 'va_gene', 'vb_gene', 'ja_gene', 'jb_gene'
]
#4
tr.deduplicate()
#5
tr._tcrdist_legacy_method_alpha_beta()
#6
distA = tr.dist_a
distB = tr.dist_b
assert np.all(((distA + distB) - tr.paired_tcrdist) == 0)
# 1
criteria = tr.clone_df.epitope == "PA"
clone_df_subset = tr.clone_df[criteria]
# 2
distA_subset = distA.loc[clone_df_subset.clone_id,
clone_df_subset.clone_id].copy()
distB_subset = distB.loc[clone_df_subset.clone_id,
clone_df_subset.clone_id].copy()
# 3
ts = TCRsubset(clone_df_subset,
organism="mouse",
epitopes=["PA"],
epitope="PA",
chains=["A", "B"],
dist_a=distA_subset,
dist_b=distB_subset)
# ts.find_motif()
cnames = [
"file_type", "count", "expect_random", "expect_nextgen", "chi_squared",
"nfixed", "showmotif", "num", "othernum", "overlap", "ep", "ab",
"nseqs", "v_rep_counts", "j_rep_counts"
]
motif_fn = 'tcrdist/test_files/mouse_pairseqs_v1_parsed_seqs_probs_mq20_clones_cdr3_motifs_PA.log'
x = open(motif_fn, "r").readlines()
ts.motif_df = pd.DataFrame([l.split() for l in x], columns=cnames)
i = 0
row = ts.motif_df.iloc[i, :].to_dict()
motif_list = list()
motif_logo = list()
for i, row in ts.motif_df.iterrows():
StoreIOMotif_instance = ts.eval_motif(row)
motif_list.append(StoreIOMotif_instance)
motif_logo.append(
plot_pwm(StoreIOMotif_instance,
create_file=False,
my_height=200,
my_width=600))
if i > 1:
break
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))