MassiR: A method for predicting the sex of samples in gene expression microarray datasets

Sam Buckberry, Stephen J. Bent, Tina Bianco-Miotto, Claire T. Roberts

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)
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High-throughput gene expression microarrays are currently the most efficient method for transcriptome-wide expression analyses. Consequently, gene expression data available through public repositories have largely been obtained from microarray experiments. However, the metadata associated with many publicly available expression microarray datasets often lacks sample sex information, therefore limiting the reuse of these data in new analyses or larger meta-analyses where the effect of sex is to be considered. Here, we present the massiR package, which provides a method for researchers to predict the sex of samples in microarray datasets. Using information from microarray probes representing Y chromosome genes, this package implements unsupervised clustering methods to classify samples into male and female groups, providing an efficient way to identify or confirm the sex of samples in mammalian microarray datasets.

Original languageEnglish
Pages (from-to)2084-2085
Number of pages2
Issue number14
Publication statusPublished - 15 Jul 2014
Externally publishedYes

Bibliographical note

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]


  • massIR
  • Pedicting sex of samples
  • Gene expression
  • Microarray datasets


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