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_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_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_initialization_of_TCRsubset_alpha_beta_case():
"""
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)
TCRsubset(clone_df=clone_df_subset,
organism="mouse",
epitopes=["PA"],
epitope="PA",
chains=["A", "B"],
dist_a=dist_a_subset,
dist_b=dist_b_subset)
def test_ng_tcrs_BOnly():
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=["B"],
dist_a=da,
dist_b=db)
tm = TCRMotif(clones_df=ts.tcr_motif_clones_df(),
organism=ts.organism,
chains=ts.chains,
epitopes=ts.epitopes,
db_file="alphabeta_db.tsv")
ng_tcrs = dict()
# Default Behavior
for chain in tm.chains: #['A','B']:
next_gen_ref = tm.generate_background_set(
chain=chain,
ng_log_path=paths.path_to_current_db_files(db_file=tm.db_file),
ng_log_file='new_nextgen_chains_{}_{}.tsv'.format(
tm.organism, chain))
ng_tcrs[chain] = next_gen_ref
assert isinstance(ng_tcrs['B'], dict)
assert set(ng_tcrs['B'].keys()) == set([
'TRBV29*01', 'TRBV16*01', 'TRBV13-2*01', 'TRBV13-1*01', 'TRBV3*01',
'TRBV3*02', 'TRBV17*01', 'TRBV15*01', 'TRBV19*01', 'TRBV19*03',
'TRBV4*01', 'TRBV4*02', 'TRBV23*01', 'TRBV13-2*05', 'TRBV13-3*01',
'TRBV12-1*01', 'TRBV12-2*01', 'TRBV2*01', 'TRBV21*01', 'TRBV5*01',
'TRBV8*01', 'TRBV1*01', 'TRBV1*02', 'TRBV26*02', 'TRBV24*01',
'TRBV26*01', 'TRBV14*01', 'TRBV24*02', 'TRBV31*01', 'TRBV5*05',
'TRBV9*01', 'TRBV20*01', 'TRBV30*01'
])
def test_initialization_of_TCRsubset_alpha_case():
"""
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_a_gene', 'j_a_gene', 'cdr3_a_aa',
'cdr1_a_aa', 'cdr2_a_aa', 'pmhc_a_aa', 'cdr3_a_nucseq', 'count',
'va_countreps', 'ja_countreps', 'va_gene', 'ja_gene'
]]
ts = TCRsubset(clone_df=df,
organism="mouse",
epitopes=["PA"],
epitope="PA",
chains=["alpha"],
dist_a=dist_a_subset)
assert (isinstance(ts.clone_df.vb_gene, pd.Series))
assert (ts.clone_df.vb_gene.iloc[0] == "TRBV1*01")
def test_initialization_of_TCRsubset_beta_case():
"""
Test that we can create a TCRsubset from just beta chain files.
For this to work, fake alpha sequences will have to be created:
"""
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'
]]
ts = TCRsubset(clone_df=df,
organism="mouse",
epitopes=["PA"],
epitope="PA",
chains=["beta"],
dist_b=dist_b_subset)
assert (isinstance(ts.clone_df.va_gene, pd.Series))
assert (ts.clone_df.va_gene.iloc[0] == "TRAV10*01")
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))
def test_ng_tcrs_AOnly():
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"],
dist_a=da,
dist_b=None)
tm = TCRMotif(clones_df=ts.tcr_motif_clones_df(),
organism=ts.organism,
chains=ts.chains,
epitopes=ts.epitopes,
db_file="alphabeta_db.tsv")
ng_tcrs = dict()
# Default Behavior
for chain in tm.chains: #['A','B']:
next_gen_ref = tm.generate_background_set(
chain=chain,
ng_log_path=paths.path_to_current_db_files(db_file=tm.db_file),
ng_log_file='new_nextgen_chains_{}_{}.tsv'.format(
tm.organism, chain))
ng_tcrs[chain] = next_gen_ref
assert isinstance(
ng_tcrs['A'], dict
) #== set(['TRGV2*01', 'TRGV5P*01', 'TRGV5P*02', 'TRGV8*01', 'TRGV4*01', 'TRGV11*01', 'TRGV10*01', 'TRGV1*01', 'TRGV3*01', 'TRGV5*01', 'TRGV9*02', 'TRGV9*01'])
assert set(ng_tcrs['A'].keys()) == set([
'TRAV14D-3/DV8*02', 'TRAV14-1*02', 'TRAV14-2*01', 'TRAV14D-1*01',
'TRAV14D-2*01', 'TRAV14D-2*02', 'TRAV14-3*01', 'TRAV14-3*02',
'TRAV14D-3/DV8*01', 'TRAV6-7/DV9*02', 'TRAV6-6*01', 'TRAV6-7/DV9*01',
'TRAV6-7/DV9*04', 'TRAV6D-6*01', 'TRAV6-5*01', 'TRAV6D-7*01',
'TRAV13-1*01', 'TRAV13D-4*01', 'TRAV13-3*01', 'TRAV10*01', 'TRAV2*01',
'TRAV6-4*01', 'TRAV3D-3*02', 'TRAV3-1*01', 'TRAV3-4*01', 'TRAV4D-4*03',
'TRAV4D-3*01', 'TRAV4D-4*01', 'TRAV4-3*01', 'TRAV4-2*01',
'TRAV4-4/DV10*01', 'TRAV4D-3*03', 'TRAV5-4*01', 'TRAV5D-4*02',
'TRAV3-3*01', 'TRAV12-3*04', 'TRAV4D-4*04', 'TRAV13-4/DV7*03',
'TRAV16*01', 'TRAV9N-3*01', 'TRAV9N-2*01', 'TRAV9D-4*03',
'TRAV9D-3*01', 'TRAV9-1*01', 'TRAV9D-2*01', 'TRAV9D-4*04',
'TRAV6-1*01', 'TRAV14-1*01', 'TRAV7-2*01', 'TRAV12D-1*01',
'TRAV12-2*01', 'TRAV12N-3*01', 'TRAV16D/DV11*03', 'TRAV12-1*01',
'TRAV12-3*01', 'TRAV12D-3*02', 'TRAV12D-2*01', 'TRAV8-2*01',
'TRAV13D-2*01', 'TRAV13-4/DV7*02', 'TRAV19*01', 'TRAV21/DV12*01',
'TRAV7-5*01', 'TRAV7-5*03', 'TRAV11*01', 'TRAV11N*01', 'TRAV11*02',
'TRAV12D-1*05', 'TRAV4D-4*02', 'TRAV7-6*02', 'TRAV7-3*01',
'TRAV6D-6*03', 'TRAV7-3*02', 'TRAV15D-2/DV6D-2*04',
'TRAV15-2/DV6-2*02', 'TRAV15-2/DV6-2*01', 'TRAV15D-2/DV6D-2*01',
'TRAV15D-2/DV6D-2*02', 'TRAV15D-1/DV6D-1*04', 'TRAV15-1/DV6-1*01',
'TRAV17*02', 'TRAV17*01', 'TRAV12-1*05', 'TRAV7N-5*01',
'TRAV15D-1/DV6D-1*01', 'TRAV7-4*01', 'TRAV12D-2*02', 'TRAV9-4*01',
'TRAV9-3*01', 'TRAV6-2*01', 'TRAV8-1*01', 'TRAV13D-1*03',
'TRAV13-2*01', 'TRAV6-7/DV9*08', 'TRAV12D-1*03', 'TRAV12-3*02',
'TRAV7-6*01', 'TRAV4-2*02', 'TRAV1*01', 'TRAV5-1*01', 'TRAV8-1*02',
'TRAV15D-1/DV6D-1*05', 'TRAV7-1*01', 'TRAV9-2*01', 'TRAV13-5*01',
'TRAV6-6*03', 'TRAV12-1*02', 'TRAV12D-3*01', 'TRAV6-4*02',
'TRAV6-1*02', 'TRAV20*01', 'TRAV14D-3/DV8*05', 'TRAV18*01',
'TRAV7D-2*03', 'TRAV16*04', 'TRAV7-5*02', 'TRAV4D-2*01',
'TRAV13D-2*02', 'TRAV5D-2*01', 'TRAV5-2*01', 'TRAV16*03'
])
def test_ng_tcrs_DOnly():
ts = TCRsubset(clone_df=clone_df_subset_d,
organism="human",
epitopes=["X"],
epitope="X",
chains=["delta"],
dist_d=dist_d_subset)
tcr_motif_delta_input = pd.DataFrame({
'subject': {
53: 'SRR5130260.1',
54: 'SRR5130260.1',
55: 'SRR5130260.1',
56: 'SRR5130260.1',
57: 'SRR5130260.1',
58: 'SRR5130260.1',
59: 'SRR5130260.1',
60: 'SRR5130260.1',
61: 'SRR5130260.1',
62: 'SRR5130260.1',
63: 'SRR5130260.1',
64: 'SRR5130260.1',
65: 'SRR5130260.1'
},
'epitope': {
53: 'X',
54: 'X',
55: 'X',
56: 'X',
57: 'X',
58: 'X',
59: 'X',
60: 'X',
61: 'X',
62: 'X',
63: 'X',
64: 'X',
65: 'X'
},
'va_rep': {
53: 'TRGV1*01',
54: 'TRGV1*01',
55: 'TRGV1*01',
56: 'TRGV1*01',
57: 'TRGV1*01',
58: 'TRGV1*01',
59: 'TRGV1*01',
60: 'TRGV1*01',
61: 'TRGV1*01',
62: 'TRGV1*01',
63: 'TRGV1*01',
64: 'TRGV1*01',
65: 'TRGV1*01'
},
'ja_rep': {
53: 'TRGJ1*01',
54: 'TRGJ1*01',
55: 'TRGJ1*01',
56: 'TRGJ1*01',
57: 'TRGJ1*01',
58: 'TRGJ1*01',
59: 'TRGJ1*01',
60: 'TRGJ1*01',
61: 'TRGJ1*01',
62: 'TRGJ1*01',
63: 'TRGJ1*01',
64: 'TRGJ1*01',
65: 'TRGJ1*01'
},
'vb_rep': {
53: 'TRDV2*01',
54: 'TRDV2*01',
55: 'TRDV2*01',
56: 'TRDV2*01',
57: 'TRDV2*01',
58: 'TRDV2*01',
59: 'TRDV2*01',
60: 'TRDV2*01',
61: 'TRDV2*01',
62: 'TRDV2*01',
63: 'TRDV2*01',
64: 'TRDV2*01',
65: 'TRDV2*01'
},
'jb_rep': {
53: 'TRDJ1*01',
54: 'TRDJ1*01',
55: 'TRDJ1*01',
56: 'TRDJ1*01',
57: 'TRDJ2*01',
58: 'TRDJ2*01',
59: 'TRDJ2*01',
60: 'TRDJ2*01',
61: 'TRDJ2*01',
62: 'TRDJ2*01',
63: 'TRDJ2*01',
64: 'TRDJ2*01',
65: 'TRDJ2*01'
},
'cdr3a': {
53: 'CATWAKNYYKKLF',
54: 'CATWAKNYYKKLF',
55: 'CATWAKNYYKKLF',
56: 'CATWAKNYYKKLF',
57: 'CATWAKNYYKKLF',
58: 'CATWAKNYYKKLF',
59: 'CATWAKNYYKKLF',
60: 'CATWAKNYYKKLF',
61: 'CATWAKNYYKKLF',
62: 'CATWAKNYYKKLF',
63: 'CATWAKNYYKKLF',
64: 'CATWAKNYYKKLF',
65: 'CATWAKNYYKKLF'
},
'cdr3b': {
53: 'CACHRGTDTDKLIF',
54: 'CACDKNGGYVRYTDKLIF',
55: 'CACDTVGIPDKLIF',
56: 'CACVRLPLRGRPYTDKLIF',
57: 'CACDNWGALTAQLFF',
58: 'CACDTILGDITLTAQLFF',
59: 'CACDTGRGTLTAQLFF',
60: 'CACDTWGMTAQLFF',
61: 'CACDTGGALTAQLFF',
62: 'CACDIRDTRVLTAQLFF',
63: 'CACDIVLGDPSLTAQLFF',
64: 'CACDHLLGDTAQLFF',
65: 'CACDPVTGGSLTAQLFF'
}
})
assert np.all(ts.tcr_motif_clones_df(gdmode=True) == tcr_motif_delta_input)
tm = TCRMotif(clones_df=ts.tcr_motif_clones_df(gdmode=True),
organism=ts.organism,
chains=ts.chains,
epitopes=ts.epitopes,
db_file="gammadelta_db.tsv")
ng_tcrs = dict()
for chain in tm.chains:
next_gen_ref = tm.generate_background_set(
chain=chain,
ng_log_path=paths.path_to_current_db_files(db_file=tm.db_file),
ng_log_file='new_nextgen_chains_{}_{}.tsv'.format(
tm.organism, chain))
ng_tcrs[chain] = next_gen_ref
# Notice that 'B' represents 'delta'
# Notice that 'A' represents 'gamma'
ng_tcrs = dict()
for chain in tm.chains:
next_gen_ref = tm.generate_background_set(
chain=chain,
ng_log_path=paths.path_to_current_db_files(db_file=tm.db_file),
ng_log_file='new_nextgen_chains_{}_{}.tsv'.format(
tm.organism, chain))
ng_tcrs[chain] = next_gen_ref
assert set(ng_tcrs['B'].keys()) == set([
'TRDV2*01', 'TRDV3*01', 'TRDV1*01', 'TRAV38-2/DV8*01', 'TRAV22*01',
'TRAV29/DV5*01', 'TRAV41*01', 'TRAV39*01', 'TRAV14/DV4*02',
'TRAV40*01', 'TRAV23/DV6*01', 'TRAV34*01', 'TRAV38-1*01',
'TRAV26-2*01', 'TRAV26-1*01', 'TRAV19*01', 'TRAV35*01', 'TRAV17*01',
'TRAV20*01', 'TRAV21*01', 'TRAV36/DV7*01', 'TRAV14/DV4*01',
'TRAV9-2*01', 'TRAV24*01', 'TRAV30*01', 'TRAV38-1*03', 'TRAV38-1*02',
'TRAV36/DV7*02', 'TRAV12-3*01', 'TRAV27*01', 'TRAV8-3*01', 'TRAV16*01',
'TRAV13-1*01', 'TRAV30*03', 'TRAV8-4*01', 'TRAV8-4*06', 'TRAV8-2*01',
'TRAV10*01', 'TRAV8-4*07', 'TRAV8-6*01', 'TRAV25*01', 'TRAV9-1*01',
'TRAV36/DV7*03', 'TRAV6*01', 'TRAV13-2*01', 'TRAV8-7*01'
])