SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups.

Abstract:

BACKGROUND:To understand biology and differences among various tissues or cell types, one typically searches for molecular features that display characteristic abundance patterns. Several specificity metrics have been introduced to identify tissue-specific molecular features, but these either require an equal number of replicates per tissue or they can't handle replicates at all. RESULTS:We describe a non-parametric specificity score that is compatible with unequal sample group sizes. To demonstrate its usefulness, the specificity score was calculated on all GTEx samples, detecting known and novel tissue-specific genes. A webtool was developed to browse these results for genes or tissues of interest. An example python implementation of SPECS is available at https://github.com/celineeveraert/SPECS. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be. CONCLUSIONS:SPECS is a non-parametric method that identifies known and novel specific-expressed genes. In addition, SPECS could be adopted for other features and applications.

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Everaert C,Volders PJ,Morlion A,Thas O,Mestdagh P

doi

10.1186/s12859-020-3407-z

subject

Has Abstract

pub_date

2020-02-17 00:00:00

pages

58

issue

1

issn

1471-2105

pii

10.1186/s12859-020-3407-z

journal_volume

21

pub_type

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