title: Cross-correlation of spectral count ranking to validate quantitative proteome measurements.
abstract: The measurement of change in biological systems through protein quantification is a central theme in modern biosciences and medicine. Label-free MS-based methods have greatly increased the ease and throughput in performing this task. Spectral counting is one such method that uses detected MS2 peptide fragmentation ions as a measure of the protein amount. The method is straightforward to use and has gained widespread interest. Additionally reports on new statistical methods for analyzing spectral count data appear at regular intervals, but a systematic evaluation of these is rarely seen. In this work, we studied how similar the results are from different spectral count data analysis methods, given the same biological input data. For this, we chose the algorithms Beta Binomial, PLGEM, QSpec, and PepC to analyze three biological data sets of varying complexity. For analyzing the capability of the methods to detect differences in protein abundance, we also performed controlled experiments by spiking a mixture of 48 human proteins in varying concentrations into a yeast protein digest to mimic biological fold changes. In general, the agreement of the analysis methods was not particularly good on the proteome-wide scale, as considerable differences were found between the different algorithms. However, we observed good agreements between the methods for the top abundance changed proteins, indicating that for a smaller fraction of the proteome changes are measurable, and the methods may be used as valuable tools in the discovery-validation pipeline when applying a cross-validation approach as described here. Performance ranking of the algorithms using samples of known composition showed PLGEM to be superior, followed by Beta Binomial, PepC, and QSpec. Similarly, the normalized versions of the same method, when available, generally outperformed the standard ones. Statistical detection of protein abundance differences was strongly influenced by the number of spectra acquired for the protein and, correspondingly, its molecular mass.
authors.affiliation: Turku Centre for Biotechnology, University of Turku and Åbo Akademi University , 20520 Turku, Finland.
authors.forename: Olli
authors.initials: O
authors.lastname: Kannaste
authors.forename: Tomi
authors.initials: T
authors.lastname: Suomi
authors.forename: Jussi
authors.initials: J
authors.lastname: Salmi
authors.forename: Esa
authors.initials: E
authors.lastname: Uusipaikka
authors.forename: Olli
authors.initials: O
authors.lastname: Nevalainen
authors.forename: Garry L
authors.initials: GL
authors.lastname: Corthals
mesh_terms: D000465:Algorithms; D000818:Animals; D002853:Chromatography, Liquid / Q000379:methods; D016000:Cluster Analysis; D005656:Fungal Proteins; D006801:Humans; D011506:Proteins / Q000032:analysis* / Q000737:chemistry; D020543:Proteome / Q000032:analysis* / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D012372:ROC Curve; D051381:Rats; D015203:Reproducibility of Results; D013552:Swine; D053719:Tandem Mass Spectrometry / Q000379:methods
chemical_list: D005656:Fungal Proteins; D011506:Proteins; D020543:Proteome