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• W1984971566 abstract "We review the fundamental principles of statistical experimental design, and their application to quantitative mass spectrometry-based proteomics. We focus on class comparison using Analysis of Variance (ANOVA), and discuss how randomization, replication and blocking help avoid systematic biases due to the experimental procedure, and help optimize our ability to detect true quantitative changes between groups. We also discuss the issues of pooling multiple biological specimens for a single mass analysis, and calculation of the number of replicates in a future study. When applicable, we emphasize the parallels between designing quantitative proteomic experiments and experiments with gene expression microarrays, and give examples from that area of research. We illustrate the discussion using theoretical considerations, and using real-data examples of profiling of disease." @default.
• W1984971566 created "2016-06-24" @default.
• W1984971566 creator A5069976109 @default.
• W1984971566 creator A5088073215 @default.
• W1984971566 date "2009-03-27" @default.
• W1984971566 modified "2023-10-17" @default.
• W1984971566 title "Statistical Design of Quantitative Mass Spectrometry-Based Proteomic Experiments" @default.
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• W1984971566 doi "https://doi.org/10.1021/pr8010099" @default.
• W1984971566 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/19222236" @default.
• W1984971566 hasPublicationYear "2009" @default.
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