Utility function to run limma differential expression analysis

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

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.

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.

Examples

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