Analysis of Proteomic Data

Chapter
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Abstract

Whether you are a proteomics specialist or simply an end-user of proteomic data, the day will come when you sit down with your dataset, typically a list of proteins or protein clusters whose abundance change in one or more experimental groups. This protein change is often represented as a ratio or fold-change. When the euphoria wears off, the nagging questions set in. How accurate are your data, really? How confident are you in these changes; are they statistically significant? If so, by what statistical test? Are you sure the test is suitable for your data? How would you know? Or perhaps more importantly, as a graduate student, would you spend the next year following up on a proteomic lead? As principal investigator, should you reallocate substantial resources to a new line of enquiry? Given the risk of squandering time and money on false leads or dismissing a nugget that could change existing paradigms, delving more deeply into the principles of robust proteomic analysis, however daunting at first blush, is a good investment.

Keywords

Experimental design Protein quantitation Differential regulation Statistical inference Empirical Bayes Significance Multiple comparison correction Robustness 
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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  2. 2.Laboratory of Cardiovascular Biochemistry, Division of Cardiology, Department of MedicineThe Johns Hopkins University School of MedicineBaltimoreUSA

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