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

Buckberry, Sam, Bent, Stephen J., Bianco-Miotto, Tina and Roberts, Claire T. (2014) MassiR: a method for predicting the sex of samples in gene expression microarray datasets. Bioinformatics, 30 14: 2084-2085. doi:10.1093/bioinformatics/btu161

Author Buckberry, Sam
Bent, Stephen J.
Bianco-Miotto, Tina
Roberts, Claire T.
Title MassiR: a method for predicting the sex of samples in gene expression microarray datasets
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1460-2059
Publication date 2014-07-15
Year available 2014
Sub-type Article (original research)
DOI 10.1093/bioinformatics/btu161
Open Access Status DOI
Volume 30
Issue 14
Start page 2084
End page 2085
Total pages 2
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Language eng
Formatted 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.
Keyword Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Biotechnology & Applied Microbiology
Computer Science
Mathematical & Computational Biology
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID TBM APP1030945
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 6 times in Scopus Article | Citations
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Created: Tue, 31 May 2016, 21:12:10 EST by Stephen Bent on behalf of Institute for Molecular Bioscience