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Uncertainty Quantification of RNA-Seq Co-expression Networks
By Lance Pflieger and Julio Facelli, Department of Biomedical Informatics
Systems biology utilizes the complex and copious data originating from the “omics” fields to increase understanding of biology by studying interactions among biological entities. Gene co-expression network analysis is a systems biology technique derived from graph theory that uses RNA expression data to infer functional similar genes or regulatory pathways. Gene co-expression network analysis is a computationally intensive process that requires matrix operations on tens-of-thousands of genes/transcripts. This technique has been useful in drug discovery, functional annotation of a gene and insight into disease pathology.
To assess the effect of uncertainty inherent with gene expression data, our group utilized CHPC resources to characterize variation in gene expression estimates and simulate a large quantity of co-expression networks based on this variation. The figure shown is a representation of network generated using WGCNA and expression data from the disease Spinocerebellar Type 2 (SCA2). The colors represent highly connected subnetworks of genes which are used to correlate similar gene clusters with a phenotypic trait. Our results show that uncertainty has a large effect on downstream results including subnetwork structure, hub genes identification and enrichment analysis. For instance, we find that the number of subnetworks correlating with the SCA2 phenotype varies from 1 to 6 subnetworks. While a small gene co-expression network analysis can be performed using only modest computation resources, the scale of resources required to perform uncertainty quantification (UQ) using Monte Carlo ensemble methods is several orders of magnitude larger, which are only available at CHPC.