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A multivariate analysis approach to the integration of proteomic and gene expression data
Author(s) -
Fagan Ailís,
Culhane Aedín C.,
Higgins Desmond G.
Publication year - 2007
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200600898
Subject(s) - computational biology , proteomics , multivariate statistics , multivariate analysis , gene expression , biology , expression (computer science) , gene , genetics , computer science , machine learning , programming language
In order to understand even the simplest cellular processes, we need to integrate proteomic, gene expression and other biomolecular data. To date, most computational approaches aimed at integrating proteomics and gene expression data use direct gene/protein correlation measures. However, due to post‐transcriptional and translational regulations, the correspondence between the expression of a gene and its protein is complicated. We apply a multivariate statistical method, co‐inertia analysis (CIA), to visualise gene and proteomic expression data stemming from the same biological samples. Principal components analysis or correspondence analysis can be used for data exploration on single datasets. CIA is then used to explore the relationships between two or more datasets. We further explore the data by projecting gene ontology (GO) information onto these plots to describe the cellular processes in action. We apply these techniques to gene expression and protein abundance data from studies of the human malarial parasite life cycle and the NCI‐60 cancer cell lines. In each case, we visualise gene expression, protein abundance and GO classes in the same low dimensional projections and identify GO classes that are likely to be of biological importance.