Exploring the Utility of Ratio-based Co-expression Networks using a GPU Implementation of Semantic Similarity

Abstract

The reduced cost of sequencing has made it feasible to acquire multi-tissue site expression data from the same patient. In the field of cancer research, this has caused an accumulation of cancer type specific tumor with matched adjacent normal expression data sets. Co-expression network analysis is a common technique used to analyze expression data; however, it is unknown whether integrating multi-tissue site data into network construction or constructing pan-cancer networks will improve gene function prediction. One method of evaluating network performance relies on semantic similarity scores; however, computing these scores is computationally intensive. Here, I develop a GPU implementation of a commonly used semantic similarity measure and evaluate its performance compared to CPU-based approaches. Next, I explore whether constructing co-expression networks using the ratio of tumor to match adjacent normal mRNA or a pan-cancer consensus network produces superior performance compared to networks constructed with tumor expression alone. The findings presented here indicate that the GPU-based approach offers significant performance improvement over CPU-based approaches. However, the ratio- and pan-cancer networks produce only a modest improvement over tumor-based networks.