D-GEX | Deep learning for gene expression inference

D-GEX is a Python library. D-GEX has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However D-GEX build file is not available. You can download it from GitHub.

Large-scale gene expression profiling has been widely used to characterize cellular states in response to various disease conditions, genetic perturbations and so on. Although the cost of whole-genome expression profiling 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 ˜1,000 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, limiting its accuracy since it does not capture complex nonlinear relationship between expression of genes. We present a deep learning method (abbreviated as DGEX) to infer the expression of target genes from the expression of landmark genes. We used the microarray-based GEO 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 linear regression with 15.33% relative improvement. A gene-wise comparative analysis shows that deep learning achieves lower error than linear regression 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 2,921 expression profiles. Deep learning still outperforms linear regression with 6.57% relative improvement, and achieves lower error in 81.31% of the target genes. This code base provides all the necessary pieces to reproduce the main results of D-GEX. If you have any questions, please email yil8@uci.edu.