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

• Published in: BMC bioinformatics Vol. 21; no. 1; p. 58
• Main Authors: Everaert, Celine, Volders, Pieter-Jan, Morlion, Annelien, Thas, Olivier, Mestdagh, Pieter
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 17.02.2020; BioMed Central; BMC
• Subjects: Analysis; Displays (Marketing); Gene expression; Gene Expression Profiling; Genes; Genetic engineering; Genetic research; GTEx; Methodology; Methods; Nonparametric statistics; Novels; Proteins; RNA; RNA-sequencing; Sample Size; Sequence Analysis, RNA; Software; Specificity scoring; Statistics, Nonparametric; Tissues; RNA-sequencing; GTEx; Specificity scoring
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-020-3407-z

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. 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. 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.