MirPLS: A partial linear structure identifier method for cancer subtyping using microRNAs

Peifeng Ruan, Shuang Wang, Hua Liang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that have been successfully identified to be differentially expressed in various cancers. However, some miRNAs were reported to be up-regulated in one subtype of a cancer but down-regulated in another, making overall associations between these miRNAs and the heterogeneous cancer non-linear. These non-linearly associated miRNAs, if identified, are thus informative for cancer subtyping. Results: Here, we propose mirPLS, a Partial Linear Structure identifier for miRNA data that simultaneously identifies miRNAs of linear or non-linear associations with cancer status when non-linearly associated miRNAs can then be used for subsequent cancer subtyping. Simulation studies showed that mirPLS can identify both non-linearly and linearly outcome-associated miRNAs more accurately than the comparison methods. Using the identified nonlinearly associated miRNAs much improves the cancer subtyping accuracy. Applications to miRNA data of three different cancer types suggest that the cancer subtypes defined by the non-linearly associated miRNAs identified by mirPLS are consistently more predictive of patient survival and more biological meaningful. Availability and implementation: The R package mirPLS is available for downloading from https://github.com/ pfruan/mirPLS.

Original languageEnglish (US)
Pages (from-to)4902-4909
Number of pages8
Issue number19
StatePublished - Oct 1 2020
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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