calculateIndividualLogFC.Rd
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 )
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 |
robust.fit | The value of the |
robust.eBayes | The value of the |
trend.eBayes | The value of the |
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 |
weights | an option matrix of weights to use in |
confint | add confidence intervals to |
with.fit | If |
use.qlf | If |
... | 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 |
as.dt | If |
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.
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.