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Bioinformatics. 2016 Jun 15;32(12):1832-9. doi: 10.1093/bioinformatics/btw074. Epub 2016 Feb 11.

Gene expression inference with deep learning.

Author information

1
Department of Computer Science, University of California, Irvine, CA 92697, USA Baidu Research-Big Data Lab, Beijing, 100085, China.
2
Department of Computer Science, University of California, Irvine, CA 92697, USA.
3
Broad Institute of MIT And Harvard, Cambridge, MA 02142, USA.
4
Department of Computer Science, University of California, Irvine, CA 92697, USA Center for Complex Biological Systems, University of California, Irvine, CA 92697, USA.

Abstract

MOTIVATION:

Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations, etc. Although the cost of whole-genome expression profiles has been dropping steadily, generating a compendium of expression profiling over thousands of samples is still very expensive. Recognizing that gene expressions are often highly correlated, researchers from the NIH LINCS program have developed a cost-effective strategy of profiling only ∼1000 carefully selected landmark genes and relying on computational methods to infer the expression of remaining target genes. However, the computational approach adopted by the LINCS program is currently based on linear regression (LR), limiting its accuracy since it does not capture complex nonlinear relationship between expressions of genes.

RESULTS:

We present a deep learning method (abbreviated as D-GEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based Gene Expression Omnibus dataset, consisting of 111K expression profiles, to train our model and compare its performance to those from other methods. In terms of mean absolute error averaged across all genes, deep learning significantly outperforms LR with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than LR in 99.97% of the target genes. We also tested the performance of our learned model on an independent RNA-Seq-based GTEx dataset, which consists of 2921 expression profiles. Deep learning still outperforms LR with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes.

AVAILABILITY AND IMPLEMENTATION:

D-GEX is available at https://github.com/uci-cbcl/D-GEX CONTACT: xhx@ics.uci.edu

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26873929
PMCID:
PMC4908320
DOI:
10.1093/bioinformatics/btw074
[Indexed for MEDLINE]
Free PMC Article

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