An insight into high-resolution mass-spectrometry data

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Mass spectrometry is a powerful tool with much promise in global proteomic studies. The discipline of statistics offers robust methodologies to extract and interpret high-dimensional mass-spectrometry data and will be a valuable contributor to the field. Here, we describe the process by which data are produced, characteristics of the data, and the analytical preprocessing steps that are taken in order to interpret the data and use it in downstream statistical analyses. Because of the complexity of data acquisition, statistical methods developed for gene expression microarray data are not directly applicable to proteomic data. Areas in need of statistical research for proteomic data include alignment, experimental design, abundance normalization, and statistical analysis.

Original languageEnglish (US)
Pages (from-to)481-500
Number of pages20
JournalBiostatistics
Volume10
Issue number3
DOIs
StatePublished - Jul 2009

Fingerprint

Mass Spectrometry
Proteomics
High Resolution
Research Design
Robust Statistics
Gene Expression
Gene Expression Data
Microarray Data
Experimental design
Data Acquisition
Research
Statistical method
Normalization
Statistical Analysis
Preprocessing
Gene expression
Statistical analysis
Statistical methods
Microarray
Alignment

Keywords

  • Experimental design
  • Fourier transform
  • Mass calibration
  • Mass spectrometry
  • Normalization

ASJC Scopus subject areas

  • Medicine(all)
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

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abstract = "Mass spectrometry is a powerful tool with much promise in global proteomic studies. The discipline of statistics offers robust methodologies to extract and interpret high-dimensional mass-spectrometry data and will be a valuable contributor to the field. Here, we describe the process by which data are produced, characteristics of the data, and the analytical preprocessing steps that are taken in order to interpret the data and use it in downstream statistical analyses. Because of the complexity of data acquisition, statistical methods developed for gene expression microarray data are not directly applicable to proteomic data. Areas in need of statistical research for proteomic data include alignment, experimental design, abundance normalization, and statistical analysis.",
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AU - Therneau, Terry M

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KW - Normalization

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