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Single sample scoring of molecular phenotypes.


ABSTRACT: BACKGROUND:Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconductor package singscore ( https://bioconductor.org/packages/singscore ). RESULTS:We use multiple cancer data sets to compare singscore against widely-used methods, including GSVA, z-score, PLAGE, and ssGSEA. Our approach does not depend upon background samples and scores are thus stable regardless of the composition and number of samples being scored. In contrast, scores obtained by GSVA, z-score, PLAGE and ssGSEA can be unstable when less data are available (NS?

SUBMITTER: Foroutan M 

PROVIDER: S-EPMC6219008 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Single sample scoring of molecular phenotypes.

Foroutan Momeneh M   Bhuva Dharmesh D DD   Lyu Ruqian R   Horan Kristy K   Cursons Joseph J   Davis Melissa J MJ  

BMC bioinformatics 20181106 1


<h4>Background</h4>Gene set scoring provides a useful approach for quantifying concordance between sample transcriptomes and selected molecular signatures. Most methods use information from all samples to score an individual sample, leading to unstable scores in small data sets and introducing biases from sample composition (e.g. varying numbers of samples for different cancer subtypes). To address these issues, we have developed a truly single sample scoring method, and associated R/Bioconducto  ...[more]

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