runDEA.Rd
This function makes use of Seurat::FindAllMarkers()
to identify
the differently expressed variables across the groups of
the grouping variable denoted in the argument across
.
See details for more.
runDEA(
object,
across,
method_de = NULL,
verbose = NULL,
base = 2,
assay_name = activeAssay(object),
...
)
runDeAnalysis(...)
An object of class SPATA2
or, in case of S4 generics,
objects of classes for which a method has been defined.
Character value or NULL. Specifies the grouping variable of interest.
Use getGroupingOptions()
to obtain all variable names that group the
barcode spots of your object in a certain manner.
Character value. Denotes the method to according to which the de-analysis is performed.
Given to argument test.use
of the Seurat::FindAllMarkers()
-function. Run SPATA::dea_methods
to obtain all valid input options.
Logical. If TRUE
, informative messages regarding
the computational progress will be printed.
(Warning messages will always be printed.)
Only relevant if the SPATA2
object contains more than
one assay: Denotes the assay of interest and thus the
molecular modality to use. Defaults to the active assay
as set by activateAssay()
.
Additional arguments given to Seurat::FindAllMarkers()
Given to corresponding arguments of Seurat::FindAllMarkers()
.
The updated input object, containing the added, removed or computed results.
This function is a wrapper around the DEA pipeline from the Seurat
package. It creates a temporary Seurat
object via Seurat::CreateSeuratObject()
,
and Seurat::SCTransform()
. Then, Seurat::FindAllMarkers()
is run. The output data.frame
is stored in the SPATA2
object which is returned at the end.
If across
and/or method_de
are vectors instead of single
values runDEA()
iterates over all combinations in a for-loop and
stores the results in the respective slots. (e.g.: If across
= 'seurat_clusters'
and method_de
= c('wilcox', 'bimod') the function computes the differently expressed genes
across all groups found in the feature variable seurat_clusters according to method wilcox and
stores the results in the respective slot. Then it does the same according to method bimod.)
The results are obtainable via getDeaResults()
, getDeaResultsDf()
and getDeaGenes()
.
library(SPATA2)
data("example_data")
object <- example_data$object_UKF269T_diet
getGroupingOptions(object)
plotSurface(object, color_by = "histology")
object <- runDEA(object, across = "histology")
# extract best marker gene for each group by lowest p-value
top_marker_genes <-
getDeaGenes(object, across = "histology", n_lowest_pval = 1)
print(top_marker_genes)
plotSurfaceComparison(object, color_by = top_marker_genes)