msmsTests

msmsTests performs statistical analysis on label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectral count data to identify differentially expressed proteins between two biological conditions.

Key Features:

• Poisson GLM regression: Applies Poisson generalized linear model regression to spectral count data when mean and variance are approximately equal.
• Quasi-likelihood GLM regression: Extends Poisson regression to accommodate overdispersion when variance exceeds the mean.
• Negative binomial (edgeR): Uses edgeR's negative binomial modeling to handle count overdispersion and differences in library sizes.
• Blocking factors: Supports inclusion of blocking factors in all three models to control for nuisance variables.

Scientific Applications:

• Differential protein expression analysis: Identifies proteins that are differentially expressed between two biological conditions using LC-MS/MS spectral counts.
• Comparative proteomics: Compares protein expression across states such as healthy versus diseased tissues or treatment versus control groups.
• Biomarker discovery and mechanism investigation: Facilitates identification of candidate biomarkers and insights into molecular mechanisms underlying disease pathology and therapeutic targeting.

Methodology:

Implements Poisson GLM, quasi-likelihood GLM, and edgeR negative binomial modeling on LC-MS/MS spectral count data with support for blocking factors; implemented in R and distributed via Bioconductor.