def test_data_type_conversion_with_reduce_file_size():
"""
Test that successfull conversion of np attributes from
np.float64 to np.int16
after calling
"""
import pandas as pd
import numpy as np
import tcrdist as td
from tcrdist import mappers
from tcrdist.repertoire import TCRrep
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()
#print(type(tr.cdr3_a_aa_pw[1,1]))
assert isinstance(tr.cdr3_a_aa_pw[1,1], np.int)
assert isinstance(tr.cdr3_b_aa_pw[1,1], np.int)
tr.reduce_file_size()
assert isinstance(tr.cdr3_a_aa_pw[1,1], np.int16)
assert isinstance(tr.cdr3_b_aa_pw[1,1], np.int16)
def generate_tr():
import pandas as pd
import numpy as np
import tcrdist as td
from tcrdist import mappers
from tcrdist.repertoire import TCRrep
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()
return tr
def test_Complete_Performance_Example():
import pandas as pd
import numpy as np
import tcrdist as td
from collections import namedtuple
from tcrdist import mappers
from tcrdist.repertoire import TCRrep
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")
mapping = mappers.tcrdist_clone_df_to_tcrdist2_mapping
tcrdist2_df = mappers.generic_pandas_mapper(df=tcrdist_clone_df,
mapping=mapping)
tr = TCRrep(cell_df=tcrdist2_df, organism="mouse")
tr.infer_cdrs_from_v_gene(chain='alpha', imgt_aligned=True)
tr.infer_cdrs_from_v_gene(chain='beta', imgt_aligned=True)
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'
]
tr.deduplicate()
tr._tcrdist_legacy_method_alpha_beta(processes=1)
distA = tr.dist_a
distB = tr.dist_b
assert np.all(((distA + distB) - tr.paired_tcrdist) == 0)
# K NEAREST NEIGHBORS
pr = namedtuple("perf", ["observed", "predicted", "dist"])
obsereved = tr.clone_df.epitope.to_list()
performance = list()
k = 5
for i, row in tr.clone_df.iterrows():
ind = (tr.clone_df.subject != row.subject
) # Index hold out all data from that subject
distances = tr.paired_tcrdist[
i, ind] # Get Distances from the ith row, holding out subject
sorted_indices = np.argsort(
distances) # Get index of storted distances small to large
sorted_epitopes = tr.clone_df.epitope.iloc[sorted_indices].to_list(
) # Get epitopes associated wtih those indices
sorted_distances = distances[
sorted_indices] # Get distances associated with those neighbors
predicted = sorted_epitopes[
0:k] # Get Predicted epitopes for K nearest neighbors
predicted_distance = sorted_distances[
0:k] # Get distances for K nearest neighbots
performance.append(
pr(obsereved[i], predicted,
predicted_distance)) # Save Performance Information
performance[1:10]
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))