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

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 . The precalculated SPECS results on the GTEx data are available through a user-friendly browser at . 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.

Jaar van publicatie:2020

Trefwoorden:Specificity scoring, RNA-sequencing, GTEx

BOF-keylabel:ja

IOF-keylabel:ja

BOF-publication weight:1

CSS-citation score:1

Auteurs:International

Authors from:Government, Higher Education, Private

Toegankelijkheid:Open