Integrative Enrichment Analysis of Intra- and Inter- Tissues’ Differentially Expressed Genes Based on Perceptron

Abstract

Recent researches in biomedicine have indicated that the molecular basis of chronic complex diseases can also exist outside the disease-impacted tissues. Differentially expressed genes between different tissues can help revealing the molecular basis of chronic diseases. Therefore, it is essential to develop new computational methods for the integrative analysis of multi-tissues gene expression data, exploring the association between differentially expressed genes in different tissues and chronic disease. To analysis of intra- and inter-tissues’ differentially expressed genes, we designed an integrative enrichment analysis method based on perceptron (IEAP). Firstly, we calculated the differential expression scores of genes using fold-change approach with intra- and inter- tissues’ gene expression data. The differential expression scores are seen as features of the corresponding gene. Next, we integrated all differential expression scores of gene to get differential expression enrichment score. Finally, we ranked the genes according to the enrichment score. Top ranking genes are candidate disease-risk genes. Computational experiments shown that genes differentially expressed between striatum and liver of normal samples are more likely to be Huntington’s disease-associated genes, and the prediction precision could be further improved by IEAP. We finally obtained five disease-associated genes, two of which have been reported to be related with Huntington’s disease.