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PairwiseDistance.rst

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Pairwise Distance

Introduction

Pairwise Distance

TCRdist for Prediction

import pandas as pd
import numpy as np
import tcrdist as td

from tcrdist import mappers
from tcrdist.repertoire import TCRrep

tcrdist_clone_fn = '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()

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]
#  [perf(observed='PA', predicted=['PA', 'm139', 'NP', 'PB1', 'M45'], dist=array([132., 150., 153., 165., 171.])),
#  perf(observed='PA', predicted=['m139', 'PA', 'PA', 'PA', 'PA'], dist=array([36., 42., 48., 60., 66.])),
#  perf(observed='PA', predicted=['PA', 'PA', 'PA', 'PA', 'PA'], dist=array([33., 38., 42., 42., 54.])),
#  perf(observed='PA', predicted=['NP', 'PA', 'PA', 'PA', 'PA'], dist=array([84., 84., 90., 90., 96.])),
#  perf(observed='PA', predicted=['PA', 'M38', 'PA', 'PA', 'PA'], dist=array([84., 84., 84., 84., 84.])),
#  perf(observed='PA', predicted=['PA', 'PA', 'PA', 'PA', 'PA'], dist=array([24., 30., 32., 42., 45.])),
#  perf(observed='PA', predicted=['PA', 'M45', 'PA', 'PA', 'PA'], dist=array([ 0., 53., 54., 56., 56.])),
#  perf(observed='PA', predicted=['NP', 'PA', 'PA', 'PA', 'PA'], dist=array([116., 116., 116., 126., 126.])),
#  perf(observed='PA', predicted=['PA', 'PA', 'PA', 'PA', 'PA'], dist=array([15., 32., 33., 51., 51.]))]