Abstract：
:Proteomic studies offer enormous potential for gaining insight into cellular dynamics and disease processes. An immediate challenge for enhancing the utility of proteomics in translational research lies in methods of handling and interpreting the large datasets generated. Publications rarely extend beyond lists of proteins, putatively altered derived from basic statistics. Here we describe two additional distinct approaches (with particular strengths and limitations) that will enhance the analysis of proteomic datasets. Arithmetic and functional cluster analyses have been performed on proteins found differentially regulated in human glioma. These two approaches highlight (i) subgroups of proteins that may be co-regulated and play a role in glioma pathophysiology, and (ii) functional protein interactions that may improve comprehension of the biological mechanisms involved. A coherent proteomic strategy which involves both arithmetic and functional clustering, (together with careful consideration of conceptual limitations), is imperative for quantitative proteomics to deliver and advance the biological understanding of disease of the CNS. A strategy which combines arithmetic analysis and bioinformatics of protein-protein interactions is both generally applicable and will facilitate the interpretation of proteomic data.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Deighton RF,Short DM,McGregor RJ,Gow AJ,Whittle IR,McCulloch Jdoi
10.1016/j.jneumeth.2009.03.013subject
Has Abstractpub_date
2009-06-15 00:00:00pages
321-9issue
2eissn
0165-0270issn
1872-678Xpii
S0165-0270(09)00164-2journal_volume
180pub_type
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