Dataset Information

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

ABSTRACT:

Unlabelled

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.

SUBMITTER: Buckberry S

PROVIDER: S-EPMC4080740 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Publications

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

Buckberry Sam S Bent Stephen J SJ Bianco-Miotto Tina T Roberts Claire T CT

Bioinformatics (Oxford, England) 20140322 14

<h4>Unlabelled</h4>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 ef ...[more]

PMID: 24659105

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