Abstract
Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterward. This chapter thus covers aspects of both statistical planning as well as the actual analysis of quantitative proteomic experiments.
Highlights
The development and improvement of experimental procedures and technical equipment for protein detection and quantification have resulted in increasing numbers of conducted experiments each yielding vast amounts of data
Mass spectrometry-based quantitative proteomics is frequently used for the search for biomarkers for many different diseases, e.g., different cancer types or neurological diseases, and different types of tissue or body fluids, e.g., blood, urine or cerebrospinal fluid [1–3]
In the following paragraphs, preprocessing steps for a quantitative proteomics data set are discussed, which are an essential preparation for the following statistical analysis
Summary
The development and improvement of experimental procedures and technical equipment for protein detection and quantification have resulted in increasing numbers of conducted experiments each yielding vast amounts of data. It is important to consider statistical issues already during the planning phase of a proteomic experiment. This can ensure an unobstructed statistical analysis that has the power to detect truly differential proteins. Issues and pitfalls in experimental design are detailed This is followed by a short section on preprocessing of the derived data. While preprocessing is a very important issue to derive reliable measures of protein quantification, it is far too broad to be covered in detail . In Subheading 4, basic statistical principles are explained These comprise statistical testing, adjusting for multiple testing, as well as sample size planning. The actual applications of statistical analyses to proteomic experiments are detailed in Subheading 5
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