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'])
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_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'])