groupComparison: Finding differentially abundant proteins across conditions in...

In lindsaypino/MSstats-patch: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments

Description

Tests for significant changes in protein abundance across conditions based on a family of linear mixed-effects models in targeted Selected Reaction Monitoring (SRM), Data-Dependent Acquisition (DDA or shotgun), and Data-Independent Acquisition (DIA or SWATH-MS) experiment. It is applicable to multiple types of sample preparation, including label-free workflows, workflows that use stable isotope labeled reference proteins and peptides, and workflows that use fractionation. Experimental design of case-control study (patients are not repeatedly measured) or time course study (patients are repeatedly measured) is automatically determined based on proper statistical model.

Usage

groupComparison(contrast.matrix=contrast.matrix, data=data)	

Arguments

contrast.matrix	comparison between conditions of interests.
data	name of the (output of dataProcess function) data set.

Details

• contrast.matrix : comparison of interest. Based on the levels of conditions, specify 1 or -1 to the conditions of interests and 0 otherwise. The levels of conditions are sorted alphabetically. Command levels(QuantData$ProcessedData$GROUP_ORIGINAL) can illustrate the actual order of the levels of conditions.

The underlying model fitting functions are lm and lmer for the fixed effects model and mixed effects model, respectively.

The input of this function is the quantitative data from function (dataProcess).

Warning

When a feature is missing completely in a condition or a MS run, a warning message is sent to the console notifying the user of the missing feature. Additional filtering or imputing process is required before model fitting.

Author(s)

Ching-Yun Chang, Meena Choi, Olga Vitek.

Maintainer: Meena Choi ([e-mail])

References

Meena Choi, Ching-Yun Chang, Timothy Clough, Daniel Broudy, Trevor Killeen, Brendan MacLean and Olga Vitek. "MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments" Bioinformatics, 30(17):2524-2526, 2014.

Ching-Yun Chang, Paola Picotti, Ruth Huttenhain, Viola Heinzelmann-Schwarz, Marko Jovanovic, Ruedi Aebersold, Olga Vitek. "Protein significance analysis in selected reaction monitoring (SRM) measurements." Molecular & Cellular Proteomics, 11:M111.014662, 2012.

Timothy Clough, Safia Thaminy, Susanne Ragg, Ruedi Aebersold, Olga Vitek. "Statistical protein quantification and significance analysis in label-free LC-M experiments with complex designs" BMC Bioinformatics, 13:S16, 2012.

Examples

# Consider quantitative data (i.e. QuantData) from yeast study with ten time points of interests, 
# three biological replicates, and no technical replicates. 
# It is a time-course experiment and we attempt to compare differential abundance
# between time 1 and 7 in a set of targeted proteins. 
# In this label-based SRM experiment, MSstats uses the fitted model with expanded scope of 
# Biological replication.  

QuantData<-dataProcess(SRMRawData)
head(QuantData$ProcessedData)

levels(QuantData$ProcessedData$GROUP_ORIGINAL)
comparison<-matrix(c(-1,0,0,0,0,0,1,0,0,0),nrow=1)
row.names(comparison)<-"T7-T1"

# Tests for differentially abundant proteins with models:
# label-based SRM experiment with expanded scope of biological replication.

testResultOneComparison<-groupComparison(contrast.matrix=comparison, data=QuantData)

# table for result
testResultOneComparison$ComparisonResult

lindsaypino/MSstats-patch documentation built on May 24, 2019, 6 p.m.