title: Identification of differentially expressed peptides in high-throughput proteomics data.
abstract: With the advent of high-throughput proteomics, the type and amount of data pose a significant challenge to statistical approaches used to validate current quantitative analysis. Whereas many studies focus on the analysis at the protein level, the analysis of peptide-level data provides insight into changes at the sub-protein level, including splice variants, isoforms and a range of post-translational modifications. Statistical evaluation of liquid chromatography-mass spectrometry/mass spectrometry peptide-based label-free differential data is most commonly performed using a t-test or analysis of variance, often after the application of data imputation to reduce the number of missing values. In high-throughput proteomics, statistical analysis methods and imputation techniques are difficult to evaluate, given the lack of gold standard data sets. Here, we use experimental and resampled data to evaluate the performance of four statistical analysis methods and the added value of imputation, for different numbers of biological replicates. We find that three or four replicates are the minimum requirement for high-throughput data analysis and confident assignment of significant changes. Data imputation does increase sensitivity in some cases, but leads to a much higher actual false discovery rate. Additionally, we find that empirical Bayes method (limma) achieves the highest sensitivity, and we thus recommend its use for performing differential expression analysis at the peptide level.
authors.affiliation: Department of Viroscience, Erasmus MC, CA Rotterdam, Netherlands.
authors.forename: Michiel P
authors.initials: MP
authors.lastname: van Ooijen
authors.affiliation: Department of Biostatistics and Research Support, Julius Center, UMC Utrecht, Netherlands.
authors.forename: Victor L
authors.initials: VL
authors.lastname: Jong
authors.affiliation: Julius Center for Health Sciences and Primary Care of the University Medical Center Utrecht, Netherlands.
authors.forename: Marinus J C
authors.initials: MJC
authors.lastname: Eijkemans
authors.affiliation: Biomolecular Mass Spectrometry and Proteomics, Utrecht University, Netherlands.
authors.forename: Albert J R
authors.initials: AJR
authors.lastname: Heck
authors.affiliation: Department of Viroscience, Erasmus MC, CA Rotterdam, Netherlands.
authors.forename: Arno C
authors.initials: AC
authors.lastname: Andeweg
authors.affiliation: Biomolecular Mass Spectrometry Group, Utrecht University, Netherlands.
authors.forename: Nadine A
authors.initials: NA
authors.lastname: Binai
authors.affiliation: Department of Viroscience, Erasmus MC, CA Rotterdam, Netherlands.
authors.forename: Henk-Jan
authors.initials: HJ
authors.lastname: van den Ham
mesh_terms: D001499:Bayes Theorem; D002853:Chromatography, Liquid; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030562:Databases, Protein / Q000706:statistics & numerical data; D006801:Humans; D010455:Peptides / Q000235:genetics* / Q000378:metabolism*; D040081:Protein Array Analysis / Q000706:statistics & numerical data; D040901:Proteomics / Q000379:methods* / Q000706:statistics & numerical data; D020539:Sequence Analysis, Protein / Q000379:methods / Q000706:statistics & numerical data; D053719:Tandem Mass Spectrometry
chemical_list: D010455:Peptides