Combined Statistical Analyses of Peptide Intensities and Peptide Occurrences Improves Identification of Significant Peptides from MS-Based Proteomics Data

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 quit...

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Published inJournal of proteome research Vol. 9; no. 11; pp. 5748 - 5756
Main Authors Webb-Robertson, Bobbie-Jo M, McCue, Lee Ann, Waters, Katrina M, Matzke, Melissa M, Jacobs, Jon M, Metz, Thomas O, Varnum, Susan M, Pounds, Joel G
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 05.11.2010
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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.
AbstractList 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.
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. Missing abundance values in LC−MS data are difficult to analyze statistically because the mechanisms by which the data are missing are unknown (processing or biological effect). We present a new approach that pairs a test of independence on missing data to discern qualitative difference across treatment groups with traditional statistical tests that evaluate quantitative differences. The combination of these two statistics yields a more robust statistical description of the data.
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.
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 peptide intensities and observations (presence/absence) of peptides makes data analysis quite challenging. The missing abundance values 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 non-random 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 and the experimental groups. We pair the G-test results evaluating independence of missing data (IMD) with a standard 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 two simulated and two real LC-MS datasets to demonstrate the robustness and sensitivity of the ANOVA-IMD approach for assigning confidence to peptides with significant differential abundance among experimental groups.
Author Waters, Katrina M
Pounds, Joel G
Webb-Robertson, Bobbie-Jo M
McCue, Lee Ann
Metz, Thomas O
Matzke, Melissa M
Varnum, Susan M
Jacobs, Jon M
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  surname: Waters
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/20831241$$D View this record in MEDLINE/PubMed
https://www.osti.gov/biblio/1000135$$D View this record in Osti.gov
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ContentType Journal Article
Copyright Copyright © 2010 American Chemical Society
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Keywords quantitative statistical analysis
Missing data
qualitative statistical analysis
imputation
peak intensity comparison
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Snippet Liquid chromatography−mass spectrometry-based (LC−MS) proteomics uses peak intensities of proteolytic peptides to infer the differential abundance of...
Liquid chromatography-mass spectrometry-based (LC-MS) proteomics uses peak intensities of proteolytic peptides to infer the differential abundance of...
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StartPage 5748
SubjectTerms Analysis of Variance
BASIC BIOLOGICAL SCIENCES
CALCULATION METHODS
CONCENTRATION RATIO
DATA ANALYSIS
Data Interpretation, Statistical
Environmental Molecular Sciences Laboratory
INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY
LIQUID COLUMN CHROMATOGRAPHY
Mass Spectrometry
MASS SPECTROSCOPY
PEPTIDES
Peptides - analysis
PROTEINS
Proteomics - methods
SENSITIVITY
Sensitivity and Specificity
STATISTICS
Title Combined Statistical Analyses of Peptide Intensities and Peptide Occurrences Improves Identification of Significant Peptides from MS-Based Proteomics Data
URI http://dx.doi.org/10.1021/pr1005247
https://www.ncbi.nlm.nih.gov/pubmed/20831241
https://search.proquest.com/docview/762684755
https://www.osti.gov/biblio/1000135
https://pubmed.ncbi.nlm.nih.gov/PMC2974810
Volume 9
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