runLimmaAnalysis {NanoTube} | R Documentation |
Use Limma to conduct a simple differential expression analysis. All groups are compared against the base.group, and empirical Bayes method is used to identify significantly differentially expressed genes. Alternatively, a design matrix can be supplied, as explained in limma::limmaUsersGuide()
runLimmaAnalysis(dat, groups = NULL, base.group = NULL, design = NULL)
dat |
NanoString data ExpressionSet, from processNanostringData |
groups |
character vector, in same order as the samples in dat. NULL if already included in 'dat' |
base.group |
the group against which other groups are compared (must be one of the levels in 'groups'). Will use the first group if NULL. |
design |
a design matrix for Limma analysis (default NULL, will do analysis based on provided 'group' data) |
The fit Limma object
example_data <- system.file("extdata", "GSE117751_RAW", package = "NanoTube") sample_info <- system.file("extdata", "GSE117751_sample_data.csv", package = "NanoTube") dat <- processNanostringData(nsFiles = example_data, sampleTab = sample_info, groupCol = "Sample_Diagnosis") # Compare the two diseases against healthy controls ("None") limmaResults <- runLimmaAnalysis(dat, base.group = "None") # You can also supply a design matrix # Generate fake batch labels batch <- rep(c(0, 1), times = ncol(dat) / 2) # Reorder groups ("None" first) group <- factor(dat$groups, levels = c("None", "Autoimmune retinopathy", "Retinitis pigmentosa")) # Design matrix including sample group and batch design <- model.matrix(~group + batch) # Analyze data limmaResults2 <- runLimmaAnalysis(dat, design = design)