Evidence map›Paper›PMID 23007599›Full record

ArticleJournal of mathematical biology2013

Methods for diversity and overlap analysis in T-cell receptor populations.

Grzegorz A Rempala, Michal Seweryn

Abstract read
In one paragraph

Article in Journal of mathematical biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.

0numbers the graph read from it
0cells of the map it votes in
86citing papers in PubMed
–field-weighted citation impact
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

86 citing papers in PubMed.

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  14. Human TCR repertoire in cancer.Cancer medicine · 2024
    Review
  15. Article
  16. Article
  17. Article
  18. Article
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  20. Article

26 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

2 authors.

Grzegorz A RempalaDepartment of Biostatistics and Cancer Research Center, Georgia Health Sciences University, Augusta, GA, 30912, USA, grempala@georgiahealth.edu.
Michal Seweryn

Funding

Statistical Methods for Analyzing Antigen Receptors DataR01CA152158 · NCI · OHIO STATE UNIVERSITY · PI REMPALA, GRZEGORZ A · 2010 to 2014
$1.1M
NCI NIH HHS R01 CA152158NCI NIH HHS R01CA-152158
6 · The paper itself

Abstract

The paper presents some novel approaches to the empirical analysis of diversity and similarity (overlap) in biological or ecological systems. The analysis is motivated by the molecular studies of highly diverse mammalian T-cell receptor (TCR) populations, and is related to the classical statistical problem of analyzing two-way contingency tables with missing cells and low cell counts. The new measures of diversity and overlap are proposed, based on the information-theoretic as well as geometric considerations, with the capacity to naturally up-weight or down-weight the rare and abundant population species. The consistent estimates are derived by applying the Good-Turing sample-coverage correction. In particular, novel consistent estimates of the Shannon entropy function and the Morisita-Horn index are provided. Data from TCR populations in mice are used to illustrate the empirical performance of the proposed methods vis a vis the existing alternatives.

Indexed as

AnimalsData Interpretation, StatisticalGenetic VariationMiceReceptors, Antigen, T-CellT-LymphocytesT-Lymphocyte SubsetsReceptors, Antigen, T-Cell

Identifiers

PMID23007599
PMCPMC3543521

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Registered trials

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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.