Differential expression analysis with global network adjustment

Gelfond, J.A., Ibrahim, J.G., Gupta, M. , Cheng, M.-H. and Cody, J.D. (2013) Differential expression analysis with global network adjustment. BMC Bioinformatics, 14(258), (doi: 10.1186/1471-2105-14-258)

[img]
Preview
Text
85826.pdf - Published Version
Available under License Creative Commons Attribution.

590kB

Abstract

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p>

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Gupta, Professor Mayetri
Authors: Gelfond, J.A., Ibrahim, J.G., Gupta, M., Cheng, M.-H., and Cody, J.D.
Subjects:Q Science > QA Mathematics
Q Science > QH Natural history > QH301 Biology
Q Science > QH Natural history > QH426 Genetics
College/School:College of Science and Engineering > School of Mathematics and Statistics
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:BMC Bioinformatics
Publisher:Biomed Central
ISSN:1471-2105
Copyright Holders:Copyright © 2013 The Authors
First Published:First published in BMC Bioinformatics 14:258
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record