run_limma {crossmeta} | R Documentation

## Linear model fitting of eset with limma.

### Description

After selecting control and test samples for a contrast, surrogate variable analysis (`sva`) and linear model fitting with lmFit is performed.

### Usage

    run_limma(
      eset,
      annot = "SYMBOL",
      svobj = list(sv = NULL),
      numsv = 0,
      filter = TRUE
    )

### Arguments

`eset` | Annotated eset created by `load_raw`.
`annot` | String, column name in fData. For duplicated values in this column, the row with the highest interquartile range across selected samples will be kept. Appropriate values are `"SYMBOL"` (default - for gene level analysis) or `"ENTREZID_HS"` (for probe level analysis).
`svobj` | Surrogate variable analysis results. Returned from run_sva.
`numsv` | Number of surrogate variables to model.
`filter` | For RNA-seq. Should genes with low counts be filtered? dseqr shiny app performs this step separately. Should be `TRUE` (default) if used outside of dseqr shiny app.

### Details

If analyses need to be repeated, previous results can be reloaded with `readRDS` and supplied to the `prev_anal` parameter. In this case, previous selections will be reused.

### Value

List with:

`fit` | result of `lmFit`.
`mod` | `model.matrix` used for `fit`

[Package crossmeta version 1.20.0 Index]