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

ABSTRACT:

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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 cla

SUBMITTER: Buckberry S 

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

REPOSITORIES: biostudies-literature