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 multiGSEA'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.