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Published on: April 9, 2019
SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups
Celine Everaert1,2, Pieter-Jan Volders3,4,5, Annelien Morlion3,4
1Center for Medical Genetics, Department of Biomolecular Medicine, Ghent University, Ghent, Belgium. celine.everaert@ugent.be.
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.
