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Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification
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Published on: August 17, 2015

Feature detection with controlled error rates in LC/MS images.

Sébastien Li-Thiao-Té1, Benno Schwikowski

  • 1CMLA, ENS Cachan, CNRs, Univer Sud, Cachan, France. Sebastien.Lithiaote@cmla.ens-cachan.fr

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|March 15, 2012
PubMed
Summary

The new Quantile M-N rule enhances peptide signal detection in LC/MS images by controlling statistical errors. This improved algorithm offers better statistical bounds for false-positive and false-negative rates.

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Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Computational Biology

Background:

  • Liquid Chromatography/Mass Spectrometry (LC/MS) is crucial for peptide identification.
  • Existing Median M-N rule algorithms lack robust statistical error control.
  • Accurate feature detection is vital for reliable LC/MS data analysis.

Purpose of the Study:

  • To extend the Median M-N rule for statistical error control in peptide detection.
  • To introduce a novel algorithm, the Quantile M-N rule, for improved feature detection.
  • To provide statistical bounds for false-positive and false-negative rates in LC/MS data.

Main Methods:

  • Developed an extension of the Median M-N rule to calculate a statistical bound for the false-positive rate.
  • Investigated the false-negative rate associated with the M-N rule.
  • Analyzed the types of signals detectable and the limit of detection.

Main Results:

  • The Quantile M-N rule provides statistical control over false-positive rates.
  • Insights into the false-negative rate and detection limits were established.
  • The algorithm demonstrates applicability to various feature detection methods.

Conclusions:

  • The Quantile M-N rule offers enhanced statistical rigor for peptide signal detection in LC/MS.
  • This method improves the reliability of feature detection by controlling both false-positive and false-negative rates.
  • The Quantile M-N rule is a versatile tool applicable to a broad range of feature detection algorithms.