Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster models

• DOI: 10.1093/bioinformatics/btn553
• Publication Date: 2008-10-31T00:34:37Z
• Authors: Simon Rogers; Mark Girolami; Walter Kolch; Katrina M. Waters; Tao Liu; Brian Thrall; H. Steven Wiley
• Keywords: Proteome

Abstract Motivation: Modern transcriptomics and proteomics enable us to survey the expression of RNAs proteins at large scales. While these data are usually generated analyzed separately, there is an increasing interest in comparing co-analyzing transcriptome proteome data. A major open question whether linked how it coordinated. Results: Here we have developed a probabilistic clustering model that permits analysis links between transcriptomic proteomic profiles sensible flexible manner. Our coupled mixture defines prior probability distribution over component which protein profile should be assigned conditioned on associated mRNA belongs to. We apply this approach dataset quantitative obtained from human breast epithelial cell line (HMEC). The results reveal complex relationship with most clusters least two clusters, vice versa. more detailed incorporating information gene function Gene Ontology database shows high correlation limited components some molecular machines, such as ribosome, adhesion complexes TCP-1 chaperonin involved folding. Availability: Matlab code available authors request. Contact: [e-mail] Supplementary information: Bioinformatics online.