biostudies-literatureUnknown21(1)Everaert CVocatioStichting Tegen KankerKom op tegen KankerFonds Wetenschappelijk Onderzoek<h4>Background</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.BMC bioinformatics58https://www.ebi.ac.uk/biostudies/studies/S-EPMC7026976biostudies-literatureSPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups.PMC7026976Morlion AEveraert CThas OMestdagh PVolders PJfalseSPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups.<h4>Background</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.2020-01-01T00:00:00Z2020 Feb2024-11-20T07:57:01.739Z2020-05-22T10:55:29ZS-EPMC70269763206637010.1186/s12859-020-3407-z