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• W2056571799 abstract "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." @default.
• W2056571799 created "2016-06-24" @default.
• W2056571799 creator A5051318124 @default.
• W2056571799 creator A5060705328 @default.
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• W2056571799 date "2007-06-01" @default.
• W2056571799 modified "2023-10-18" @default.
• W2056571799 title "A multivariate analysis approach to the integration of proteomic and gene expression data" @default.
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• W2056571799 doi "https://doi.org/10.1002/pmic.200600898" @default.
• W2056571799 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/17549791" @default.
• W2056571799 hasPublicationYear "2007" @default.
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