Journal article
Systematic noise degrades gene co-expression signals but can be corrected
S Freytag, J Gagnon-Bartsch, TP Speed, M Bahlo
BMC Bioinformatics | Published : 2015
Abstract
Background: In the past decade, the identification of gene co-expression has become a routine part of the analysis of high-dimensional microarray data. Gene co-expression, which is mostly detected via the Pearson correlation coefficient, has played an important role in the discovery of molecular pathways and networks. Unfortunately, the presence of systematic noise in high-dimensional microarray datasets corrupts estimates of gene co-expression. Removing systematic noise from microarray data is therefore crucial. Many cleaning approaches for microarray data exist, however these methods are aimed towards improving differential expression analysis and their performances have been primarily tes..
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Awarded by Australian Research Council
Funding Acknowledgements
We acknowledge Karen Oliver and Vesna Lukic for providing lists of known eplileptic encephalopathy genes and candidates. Furthermore, we would like to thank Matthew Ritchie for helpful discussions on preprocessing of gene expression data and Peter Hickey with programing advice. This work was funded in part by Australian National Health and Medical Research Council (NHMRC) program grant 490037. MB was supported by an Australian Research Council Future Fellowship (FT100100764). We also want to thank Michael Hawrylycz and Changkyu Lee at the Allen Institute of Brain Science for their patience in answering our many questions concerning the Miller et al. data.