Statistical and Computational Methods for Comparative Proteomic Profiling Using Liquid Chromatography-Tandem Mass Spectrometry
The combined method of LC-MS/MS is increasingly being used to explore differences in the proteomic composition of complex biological systems. The reliability and utility of such comparative protein expression profiling studies is critically dependent on an accurate and rigorous assessment of quantit...
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| Published in: | Molecular & cellular proteomics 2005-04, Vol.4 (4), p.419-434 |
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| Main Authors: | , |
| Format: | Article |
| Language: | eng |
| Subjects: | |
| Online Access: | Get full text |
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| Summary: | The combined method of LC-MS/MS is increasingly being used to explore differences in the proteomic composition of complex
biological systems. The reliability and utility of such comparative protein expression profiling studies is critically dependent
on an accurate and rigorous assessment of quantitative changes in the relative abundance of the myriad of proteins typically
present in a biological sample such as blood or tissue. In this review, we provide an overview of key statistical and computational
issues relevant to bottom-up shotgun global proteomic analysis, with an emphasis on methods that can be applied to improve
the dependability of biological inferences drawn from large proteomic datasets. Focusing on a start-to-finish approach, we
address the following topics: 1) low-level data processing steps, such as formation of a data matrix, filtering, and baseline
subtraction to minimize noise, 2) mid-level processing steps, such as data normalization, alignment in time, peak detection,
peak quantification, peak matching, and error models, to facilitate profile comparisons; and, 3) high-level processing steps
such as sample classification and biomarker discovery, and related topics such as significance testing, multiple testing,
and choice of feature space. We report on approaches that have recently been developed for these steps, discussing their merits
and limitations, and propose areas deserving of further research. |
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| ISSN: | 1535-9476 1535-9484 |