U.S. Department of Energy

Pacific Northwest National Laboratory

Combined statistical analyses of peptide intensities and peptide occurrences improves identification of significant peptides from MS-based proteomics data.

TitleCombined statistical analyses of peptide intensities and peptide occurrences improves identification of significant peptides from MS-based proteomics data.
Publication TypeJournal Article
Year of Publication2010
AuthorsWebb-Robertson B-JM, McCue LAnn, Waters KM, Matzke MM, Jacobs JM, Metz TO, Varnum SM, Pounds JG
JournalJ Proteome Res
KeywordsAnalysis of Variance, Data Interpretation, Statistical, Mass Spectrometry, Peptides, Proteomics, Sensitivity and Specificity
Abstract

Liquid chromatography-mass spectrometry-based (LC-MS) proteomics uses peak intensities of proteolytic peptides to infer the differential abundance of peptides/proteins. However, substantial run-to-run variability in intensities and observations (presence/absence) of peptides makes data analysis quite challenging. The missing observations in LC-MS proteomics data are difficult to address with traditional imputation-based approaches because the mechanisms by which data are missing are unknown a priori. Data can be missing due to random mechanisms such as experimental error or nonrandom mechanisms such as a true biological effect. We present a statistical approach that uses a test of independence known as a G-test to test the null hypothesis of independence between the number of missing values across experimental groups. We pair the G-test results, evaluating independence of missing data (IMD) with an analysis of variance (ANOVA) that uses only means and variances computed from the observed data. Each peptide is therefore represented by two statistical confidence metrics, one for qualitative differential observation and one for quantitative differential intensity. We use three LC-MS data sets to demonstrate the robustness and sensitivity of the IMD-ANOVA approach.

DOI10.1021/pr1005247
Alternate JournalJ. Proteome Res.
PubMed ID20831241
PubMed Central IDPMC2974810
Grant List1R011GM-084892 / GM / NIGMS NIH HHS / United States
HHSN272200800060C / / PHS HHS / United States
U54 016015 / / PHS HHS / United States
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