Dataset Information
SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups.
Ontology highlight
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.
SUBMITTER: Everaert C
PROVIDER: S-EPMC7026976 | biostudies-literature | 2020 Feb
REPOSITORIES: biostudies-literature
Publications
SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups.
BMC bioinformatics 20200217 1
<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 si ...[more]
