Statistics for Proteomics: A Review of Tools for Analyzing Experimental Data

Abstract

Most proteomics experiments make use of 'high throughput' technologies such as 2-DE, MS or protein arrays to measure simultaneously the expression levels of thousands of proteins. Such experiments yield large, high-dimensional data sets which usually reflect not only the biological but also technical and experimental factors. Statistical tools are essential for evaluating these data and preventing false conclusions. Here, an overview is given of some typical statistical tools for proteomics experiments. In particular, we present methods for data preprocessing (e.g. calibration, missing values estimation and outlier detection), comparison of protein expression in different groups (e.g. detection of differentially expressed proteins or classification of new observations) as well as the detection of dependencies between proteins (e.g. protein clusters or networks). We also discuss questions of sample size planning for some of these methods.

Characterization of the Consequences of YidC Depletion on the Inner Membrane Proteome of E. coli Using 2D Blue Native/SDS-PAGE

“…This reduces the chance of type I errors (false positives) due to mass significance to a negligible level. 49,50 This indicates that the observed effects of YidC depletion on the composition of the IMP proteome are indeed due to primary effects rather than secondary effects. This is in keeping with a recent study showing that mRNA levels of none of the IMPs that, upon YidC depletion, were negatively affected at the protein level were lowered.…”

Identification and Predictive Value of Interleukin-6 + Interleukin-10 + and Interleukin-6 − Interleukin-10 + Cytokine Patterns in ST-Elevation Acute Myocardial Infarction

“…patients; 15,16 and (2) whether there is any advantage in considering features not as the measures of single cytokines but rather as the summed activity of a module. To answer these questions, we performed: (1) data preprocessing (variance stabilization using log10) 20,21 ; (2) This modeling strategy is called functional module approach, and in our application we defined the module activity as the average of all cytokine values in the module. 15,16 Functional modules can be interpreted, from a systems biology point of view, as single logic modules (composed of small cohorts of proteins) that, while embedded in a large network of proteins, have a specific biological function.…”

Identification of Early Intestinal Neoplasia Protein Biomarkers Using Laser Capture Microdissection and MALDI MS

“…The excellent result from the hierarchical clustering is expected because the covariates used in the analysis were significantly different between study groups (35). Finally the number of peaks selected is not based on the smallest number of features that could discriminate the classes but is based on the statistical evidence; therefore, different subsets of protein peaks may achieve similar misclassification rates (36).…”