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msmsEDA & msmsTests: Label-Free Differential Expression by Spectral Counts.

Josep Gregori1, Àlex Sánchez2,3, Josep Villanueva4

  • 1Vall Hebron Research Institute (VHIR), Barcelona, Spain. josep.gregori@vhir.org.

Methods in Molecular Biology (Clifton, N.J.)
|October 29, 2022
PubMed
Summary

This study introduces msmsTests, an R package for identifying differentially expressed proteins in label-free LC-MS/MS data. It offers three statistical tests and reproducibility filters for robust protein expression analysis.

Keywords:
Batch effectsBioconductorBiomarker discoveryLabel freeNormalizationReproducibilitySecretomesSpectral countsmsmsEDAmsmsTests

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) is crucial for quantitative proteomics.
  • Accurate statistical methods are needed to identify differentially expressed proteins between biological conditions using spectral count data.

Purpose of the Study:

  • To present msmsTests, an R/Bioconductor package for statistical analysis of label-free LC-MS/MS spectral count data.
  • To provide robust methods for discovering differentially expressed proteins.
  • To enhance data reproducibility through filtering options.

Main Methods:

  • Implementation of three statistical tests: Poisson GLM regression, quasi-likelihood GLM regression, and edgeR's negative binomial model.
  • Inclusion of blocking factors for controlling nuisance variables.
  • Development of a post-test filter based on minimum effect size and minimum expression levels.

Main Results:

  • The msmsTests package offers multiple statistical models for differential protein expression analysis.
  • A companion package, msmsEDA, facilitates exploratory data analysis with visualization tools.
  • Demonstration of package utility on spike-in and cancer secretome datasets.

Conclusions:

  • msmsTests provides a flexible and reproducible framework for differential protein expression analysis in label-free LC-MS/MS proteomics.
  • The integrated EDA tools aid in data quality assessment and normalization strategy evaluation.
  • These packages support robust discovery of biologically relevant protein abundance changes.