calculateIndividualLogFC: Utility function to run limma differential expression...

In lianos/sparrow: Take command of set enrichment analyses through a unified interface

Utility function to run limma differential expression analysis

Description

Utility function to run limma differential expression analysis

Usage

calculateIndividualLogFC(
  x,
  design,
  contrast = ncol(design),
  robust.fit = FALSE,
  robust.eBayes = FALSE,
  trend.eBayes = FALSE,
  treat.lfc = NULL,
  weights = NULL,
  confint = TRUE,
  with.fit = FALSE,
  use.qlf = TRUE,
  ...,
  xmeta. = NULL,
  as.dt = FALSE
)

Arguments

x	The expression object. This can be 1 column matrix if you are not running any analysis, and this function essentially is just a "pass through"
design	The design matrix for the experiment
contrast	The contrast you want to test and provide stats for. By default this tests the last column of the design matrix. If you want to test a custom contrast, this can be a contrast vector, which means that it should be as long as ncol(design) and it most-often sum to one. In the future, the user will be able to specify a range of coefficients over design to perform an ANOVA analysis, cf. Issue #11 (https://github.com/lianos/multiGSEA/issues/11).
robust.fit	The value of the robust parameter to pass down to the limma::lmFit() function. Defaults to FALSE.
robust.eBayes	The value of the robust parameter to pass down to the limma::eBayes()] function.
trend.eBayes	The value of the trend parameter to pass down to the limma::eBayes() function.
treat.lfc	If this is numeric, this activates limma's "treat" functionality and tests for differential expression against this specified log fold change threshold. This defaults to NULL.
weights	an option matrix of weights to use in limma::lmFit(). If x is an EList already, and x$weights is already defined, this argument will be ignored.
confint	add confidence intervals to topTable output (default TRUE)? Ignored if x is a DGEList.
with.fit	If TRUE, this function returns a list object with both the fit and the table of logFC statistics, otherwise just the logFC statistics table is returned.
use.qlf	If TRUE (default), will use edgeR's quasilikelihood framework for analysis, otherwise uses glmFit/glmTest.
...	parameters passed down into the relevant limma/edgeR based functions.
xmeta.	a data.frame to add meta data (symbol, primarly) to the outgoing logFC data.frame. This is used when x was a vector (pre-ranked).
as.dt	If FALSE (default), the data.frame like thing that this funciton returns will be set to a data.frame. Set this to TRUE to keep this object as a data.table

Details

This function fits linear modles (or glms) to perform differential expression analyses. If the x object is a DGEList the analysis will be performed using edgeR's quasi-likelihood framework, otherwise limma will be used for all other scenarios.

If x is a edgeR::DGEList() we require that edgeR::estimateDisp() has already been called. If you prefer to analyze rnaseq data using voom, be sure that x is the object that has been returned from a call to limma::voom() (or limma::voomWithQualityWeights().

The documentation here is speaking the language of a "limma" analysis, however for each parameter, there is an analagous function/parameter that will be delegated to.

Lastly, if x is simply a single column matrix, we assume that we are just passing a single pre-ranked vector of statistics through sparrow::seas's analysis pipelines (for use in methods like "fgsea", "cameraPR", etc.), and a logFC-like data.frame is constructed with these statistics in the logFC and t columns.

Value

If with.fit == FALSE (the default) a data.table of logFC statistics for the contrast under test. Otherwise, a list is returned with ⁠$result⁠ containing the logFC statistics, and ⁠$fit⁠ has the limma fit for the data/design/contrast under test.

Examples

vm <- exampleExpressionSet(do.voom = TRUE)
lfc <- calculateIndividualLogFC(vm, vm$design, "tumor")

lianos/sparrow documentation built on Dec. 8, 2024, 2:19 a.m.