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AMG contributed to generation of proteomic data.
BTH and GBM contributed to concept design, generation of proteomic data, and data analysis and interpretation.
There are different methods for identifying biomarkers in blood (Table 1); hence, it is important to standardize the methods of generation of proteomic data and the entire workflow.
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Background: Advances in mass spectrometry-based proteomics have enabled the incorporation of proteomic data into systems approaches to biology.
Correlated variables such as these can confound the statistical analyses of proteomic data.
A greater understanding of HCD fragmentation patterns would benefit the development of proteomic data analysis algorithms.
Nonmetric multidimensional scaling plots (NMDS) of proteomic data (normalized spectral abundance factor) from Rpom Thaps dilutions for DDA (a) and SRM (b) analyses.
An overview of the web-based resources and databases for analysis of proteomic data is also provided.
Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features.
More broadly, the availability of a generalizable, statistically principled analytical framework for proteomic analysis should facilitate the incorporation of proteomic data into systems biology studies.
Heat maps of proteomic data were drawn using the image function in software R [79].
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