runLimmaAnalysis {NanoTube} | R Documentation

## Conduct differential expression analysis

### Description

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()

### Usage

    runLimmaAnalysis(dat, groups = NULL, base.group = NULL, design = NULL)

### Arguments

`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)

### Value

The fit Limma object

### Examples

    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)

[Package NanoTube version 1.0.0 Index]