Increased Power for the Analysis of Label-Free LC-MS/MS Proteomic Data by Combining Spectral Counts and Peptide Peak Attributes

In bottom-up LC-MS/MS-based proteomics, proteins are enzymatically digested into peptides prior to query by LC-MS/MS. Thus, the information directly available from the LC-MS/MS data is at the peptide level.

If a protein-level analysis is desired, the peptide-level information must be tolled up into protein-level information. We propose a principal components analysis-based statistical method, ProPCA, for efficiently estimating relative protein abundance from bottom-up label-free LC-MS/MS data, which incorporates both spectral count information and LC-MS peptide ion peak attributes, such as peak area, volume or height. ProPCA may be used effectively with a variety of quantification platforms and is easily implemented.