run_limma: Linear model fitting of eset with limma.

In alexvpickering/crossmeta: Cross Platform Meta-Analysis of Microarray Data

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

alexvpickering/crossmeta documentation built on June 2, 2022, 7:06 a.m.