runLimma {debrowser} | R Documentation

## runLimma

### Description

Run Limma algorithm on the selected conditions. Output is to be used for the interactive display.

### Usage

    runLimma(data = NULL, columns = NULL, conds = NULL, normfact = c("none",
      "TMM", "RLE", "upperquartile"), fitType = c("ls", "robust"),
      normBet = c("none", "scale", "quantile", "cyclicloess", "Aquantile",
      "Gquantile", "Rquantile", "Tquantile"), rowsum.filter = 10)

### Arguments

`data,` | A matrix that includes all the expression raw counts, rownames has to be the gene, isoform or region names/IDs
`columns,` | is a vector that includes the columns that are going to be analyzed. These columns has to match with the given data.
`conds,` | experimental conditions. The order has to match with the column order
`normfact,` | Calculate normalization factors to scale the raw library sizes. Values can be "TMM","RLE","upperquartile","none".
`fitType,` | fitting method; "ls" for least squares or "robust" for robust regression
`normBet,` | Normalizes expression intensities so that the intensities or log-ratios have similar distributions across a set of arrays.
`rowsum.filter,` | regions/genes/isoforms with total count (across all samples) below this value will be filtered out

### Value

Limma results

### Examples

        x <- runLimma()

[Package debrowser version 1.6.8 Index]