Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/91840
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Type: Journal article
Title: MassiR: A method for predicting the sex of samples in gene expression microarray datasets
Author: Buckberry, S.
Bent, S.
Bianco-Miotto, T.
Roberts, C.
Citation: Bioinformatics, 2014; 30(14):2084-2085
Publisher: Oxford University Press
Issue Date: 2014
ISSN: 1367-4803
1460-2059
Statement of
Responsibility: 
Sam Buckberry, Stephen J. Bent, Tina Bianco-Miotto and Claire T. Roberts
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.
Keywords: Cluster Analysis; Oligonucleotide Array Sequence Analysis; Gene Expression Profiling; Software
Rights: © The Author 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), 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 journals.permissions@oup.com
RMID: 0030010268
DOI: 10.1093/bioinformatics/btu161
Grant ID: http://purl.org/au-research/grants/nhmrc/1020749
http://purl.org/au-research/grants/nhmrc/565320
Appears in Collections:Paediatrics publications

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