2013
DOI: 10.1002/pmic.201300034
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Application of meta-analysis methods for identifying proteomic expression level differences

Abstract: We present new statistical approaches for identification of proteins with expression levels that are significantly changed when applying meta-analysis to two or more independent experiments. We showed that the Euclidean distance measure has reduced risk of false positives compared to the rank product method. Our Ψ-ranking method has advantages over the traditional fold-change approach by incorporating both the fold-change direction as well as the p-value. In addition, the second novel method, Π-ranking, consid… Show more

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Cited by 2 publications
(2 citation statements)
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“…In the present proteomics study, we analyzed the blood samples of 284 subjects, generating the data resource that allowed us to reveal molecular similarities and differences within the plasma/ serum proteome in different human cancers: colorectal, pancreatic, lung, prostate, and ovarian carcinomas. To date, no current blood proteomics investigation combines multi-cancer comparisons within the same analysis; such studies rely on meta-analyses and can be confounded by variable pre-analytical factors, proteomics techniques, and measurement machines (Amess et al, 2013). To avoid experimental bias by using one universal control group for all comparisons, our study design included, for each cancer type, a matched control from each hospital center.…”
Section: Discussionmentioning
confidence: 99%
“…In the present proteomics study, we analyzed the blood samples of 284 subjects, generating the data resource that allowed us to reveal molecular similarities and differences within the plasma/ serum proteome in different human cancers: colorectal, pancreatic, lung, prostate, and ovarian carcinomas. To date, no current blood proteomics investigation combines multi-cancer comparisons within the same analysis; such studies rely on meta-analyses and can be confounded by variable pre-analytical factors, proteomics techniques, and measurement machines (Amess et al, 2013). To avoid experimental bias by using one universal control group for all comparisons, our study design included, for each cancer type, a matched control from each hospital center.…”
Section: Discussionmentioning
confidence: 99%
“…Euclidean distance is sensitive to the numeric values of protein abundances: being the absolute differences between expression values also measured by t -test statistics, it could be the natural choice to select for connecting the results to t-statistics. Among the various functions applicable to independent experiments, Euclidean distance proved practical in reducing false positives compared to the rank product method in small samples [ 31 ]. Also, in a series of works [ 32 , 33 , 34 , 35 ], the association between Euclidean distances and Spearman correlations proved to be effective in building clusters for anomaly detection in proteomics data.…”
Section: Introductionmentioning
confidence: 99%