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.
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Kammers, K., Foster, D.B., Ruczinski, I. (2016). Analysis of Proteomic Data. In: Agnetti, G., Lindsey, M., Foster, D. (eds) Manual of Cardiovascular Proteomics. Springer, Cham. https://doi.org/10.1007/978-3-319-31828-8_12
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DOI: https://doi.org/10.1007/978-3-319-31828-8_12
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