Chapter title |
Statistical Assessment of QC Metrics on Raw LC-MS/MS Data
|
---|---|
Chapter number | 22 |
Book title |
Proteomics
|
Published in |
Methods in molecular biology, December 2016
|
DOI | 10.1007/978-1-4939-6747-6_22 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6745-2, 978-1-4939-6747-6
|
Authors |
Xia Wang |
Editors |
Lucio Comai, Jonathan E. Katz, Parag Mallick |
Abstract |
Data quality assessment is important for reproducibility of proteomics experiments and reusability of proteomics data. We describe a set of statistical tools to routinely visualize and examine the quality control (QC) metrics obtained for raw LC-MS/MS data on different instrument types and mass spectrometers. The QC metrics used here are the identification free QuaMeter metrics. Statistical assessments introduced include (a) principal component analysis, (b) dissimilarity measures, (c) T (2)-chart for quality control, and (d) change point analysis. We demonstrate the workflow by a step-by-step assessment of a subset of Study 5 for the Clinical Proteomics Technology Assessment for Cancer (CPTAC) using our R functions. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 3 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 2 | 67% |
Unspecified | 1 | 33% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 2 | 67% |
Unspecified | 1 | 33% |