Evidence map›Paper›PMID 28179494›Full record

ArticleJournal of immunology (Baltimore, Md. : 1950)2017

Hierarchical Clustering Can Identify B Cell Clones with High Confidence in Ig Repertoire Sequencing Data.

Namita T Gupta, Kristofor D Adams, Adrian W Briggs, Sonia C Timberlake, Francois Vigneault, Steven H Kleinstein

Open access · bronzeAbstract read
In one paragraph

Article in Journal of immunology (Baltimore, Md. : 1950), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 114 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
114citing papers in PubMed, 1 pooled it
13.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

114 citing papers in PubMed, 1 synthesis or guideline pooled it, 178 citations in OpenAlex.

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54 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Namita T GuptaInterdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520.
Kristofor D AdamsAbVitro, Boston, MA 02210.ORCID 0000-0002-3614-794X
Adrian W BriggsAbVitro, Boston, MA 02210.
Sonia C TimberlakeAbVitro, Boston, MA 02210.
Francois VigneaultAbVitro, Boston, MA 02210.ORCID 0000-0001-5634-5794
Steven H KleinsteinInterdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520; steven.kleinstein@yale.edu.
Yale University · US

Funding

COMPUTATIONAL TOOLS FOR THE ANALYSIS OF HIGH-THROUGHPUT IMMUNOGLOBULIN SEQUENCING EXPERIMENTSR01AI104739 · NIAID · YALE UNIVERSITY · PI KLEINSTEIN, STEVEN H. · 2014 to 2022
$3.6M
YALE BIOMEDICAL SUPERCOMPUTER: NEUROSCIENCES10RR019895 · NCRR · YALE UNIVERSITY · PI WILLIAMS, KENNETH ROBERT · 2004 to 2004
$1.6M
High Performance Computing Instrumentation for Yale University Biomedical HPC CenS10RR029676 · NCRR · YALE UNIVERSITY · PI BJORNSON, ROBERT DEAN · 2011 to 2011
$588k
NCRR NIH HHS S10 RR019895NCRR NIH HHS S10 RR029676NIAID NIH HHS R01 AI104739
6 · The paper itself

Abstract

Adaptive immunity is driven by the expansion, somatic hypermutation, and selection of B cell clones. Each clone is the progeny of a single B cell responding to Ag, with diversified Ig receptors. These receptors can now be profiled on a large scale by next-generation sequencing. Such data provide a window into the microevolutionary dynamics that drive successful immune responses and the dysregulation that occurs with aging or disease. Clonal relationships are not directly measured, but they must be computationally inferred from these sequencing data. Although several hierarchical clustering-based methods have been proposed, they vary in distance and linkage methods and have not yet been rigorously compared. In this study, we use a combination of human experimental and simulated data to characterize the performance of hierarchical clustering-based methods for partitioning sequences into clones. We find that single linkage clustering has high performance, with specificity, sensitivity, and positive predictive value all >99%, whereas other linkages result in a significant loss of sensitivity. Surprisingly, distance metrics that incorporate the biases of somatic hypermutation do not outperform simple Hamming distance. Although errors were more likely in sequences with short junctions, using the entire dataset to choose a single distance threshold for clustering is near optimal. Our results suggest that hierarchical clustering using single linkage with Hamming distance identifies clones with high confidence and provides a fully automated method for clonal grouping. The performance estimates we develop provide important context to interpret clonal analysis of repertoire sequencing data and allow for rigorous testing of other clonal grouping algorithms.

Indexed as

Antibody DiversityAdaptive ImmunityBiological EvolutionB-LymphocytesClone CellsCluster AnalysisComputational BiologyComputer SimulationDatasets as TopicElectronic Data ProcessingGenetic LinkageHigh-Throughput Nucleotide SequencingHumansImmunoglobulinsSomatic Hypermutation, ImmunoglobulinImmunoglobulins

Identifiers

PMID28179494
PMCPMC5340603
OpenAlexW2594817103

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.