Single sample scoring of molecular phenotypes

Momeneh Foroutan, Dharmesh D. Bhuva, Ruqian Lyu, Kristy Horan, Joseph Cursons, Melissa J. Davis

Research output: Contribution to journalArticleOtherpeer-review

19 Citations (Scopus)


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 ( ). 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 (N S <25). The singscore method performs as well as the best performing methods in terms of power, recall, false positive rate and computational time, and provides consistently high and balanced performance across all these criteria. To enhance the impact and utility of our method, we have also included a set of functions implementing visual analysis and diagnostics to support the exploration of molecular phenotypes in single samples and across populations of data. Conclusions: The singscore method described here functions independent of sample composition in gene expression data and thus it provides stable scores, which are particularly useful for small data sets or data integration. Singscore performs well across all performance criteria, and includes a suite of powerful visualization functions to assist in the interpretation of results. This method performs as well as or better than other scoring approaches in terms of its power to distinguish samples with distinct biology and its ability to call true differential gene sets between two conditions. These scores can be used for dimensional reduction of transcriptomic data and the phenotypic landscapes obtained by scoring samples against multiple molecular signatures may provide insights for sample stratification.

Original languageEnglish
Article number404
Number of pages10
JournalBMC Bioinformatics
Issue number1
Publication statusPublished - 6 Nov 2018
Externally publishedYes


  • Dimensional reduction
  • Gene set enrichment
  • Gene set score
  • Gene signature
  • Molecular features
  • Molecular phenotypes
  • Personalised medicine
  • Single sample
  • Singscore
  • Transcriptome

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