Differential Expression Analysis in RNA-seq Data Using a Geometric Approach

Autor(es)

Tambonis, Tiago ^[1] ; Boareto, Marcelo ^[2] ; Leite, Vitor B. P. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Estadual Paulista, Inst Biociencias Letras & Ciencias Exatas, Dept Fis, BR-15054000 Sao Jose Do Rio Preto - Brazil

^[2] Swiss Fed Inst Technol, Dept Biosyst Sci & Engn D BSSE, Basel - Switzerland

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF COMPUTATIONAL BIOLOGY; v. 25, n. 11, p. 1257-1265, NOV 2018.
• Citações Web of Science: 0

Resumo

Although differential gene expression (DGE) profiling in RNA-seq is used by many researchers, new packages and pipelines are continuously being presented as a result of an ongoing investigation. In this work, a geometric approach based on Supervised Variational Relevance Learning (Suvrel) was compared with DEpackages (edgeR, DESEq, baySeq, PoissonSeq, and limma) in the DGE profiling. The Suvrel method seeks to determine the relevance of characteristics (e.g., gene or transcript) based on intraclass and interclass distances. The comparison was performed using technical and biological replicates. For technical replicates, we used receiver operating characteristic (ROC) analysis, while for the other ones, we used robustness analysis. From ROC analysis, we found that geometric approach had a better performance than the DEpackages. Particularly, for a reduced list of differentially expressed genes (DEG), we noticed that this method had a remarkable advantage in ranking of most DEG (with a specificity ranging from 1 to 0.8). From robustness analysis associated to biological replicates, we found that geometric approach has comparable performance to the DEpackages. We conclude that the geometric approach had a slight overall better performance than the other methods. Moreover, it is a simple method that does not make any assumption about the distribution associated with RNA-seq data set. From this perspective, the relevance of this study was to show that a simple method can provide as good performance as more complex methods. (AU)

• Processo FAPESP: 16/19766-1 - Relevo de superfícies de energia de macromoléculas biológicas com aplicações em biotecnologia e em biomedicina
• Beneficiário: Vitor Barbanti Pereira Leite
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/06862-7 - Estudos computacionais em enovelamento de proteínas e engenharia de enzimas envolvidas na geração de bioetanol
• Beneficiário: Vitor Barbanti Pereira Leite
• Modalidade de apoio: Auxílio à Pesquisa - Regular