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Bioinformatics. 2014 Jul 15;30(14):2084-5. doi: 10.1093/bioinformatics/btu161. Epub 2014 Mar 22.

massiR: a method for predicting the sex of samples in gene expression microarray datasets.

Author information

1
The Robinson Research Institute, School of Paediatrics and Reproductive Health, The University of Adelaide, Adelaide 5005, Australia and School of Agriculture Food and Wine, The University of Adelaide, Adelaide 5005, Australia.
2
The Robinson Research Institute, School of Paediatrics and Reproductive Health, The University of Adelaide, Adelaide 5005, Australia and School of Agriculture Food and Wine, The University of Adelaide, Adelaide 5005, AustraliaThe Robinson Research Institute, School of Paediatrics and Reproductive Health, The University of Adelaide, Adelaide 5005, Australia and School of Agriculture Food and Wine, The University of Adelaide, Adelaide 5005, Australia.

Abstract

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.

AVAILABILITY AND IMPLEMENTATION:

massiR is implemented as a Bioconductor package in R. The package and the vignette can be downloaded at bioconductor.org and are provided under a GPL-2 license.

PMID:
24659105
PMCID:
PMC4080740
DOI:
10.1093/bioinformatics/btu161
[Indexed for MEDLINE]
Free PMC Article

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