Differential expression
diffExp(object, ...) # S4 method for SingleCellExperiment diffExp(object, numerator, denominator, caller = c("edgeR", "DESeq2"), minCells = 2L, minCellsPerGene = 1L, minCountsPerCell = 1L, BPPARAM = BiocParallel::SerialParam()) # S4 method for Seurat diffExp(object, numerator, denominator, caller = c("edgeR", "DESeq2"), minCells = 2L, minCellsPerGene = 1L, minCountsPerCell = 1L, BPPARAM = BiocParallel::SerialParam())
object  Object. 

numerator 

denominator 

caller 

minCells 

minCellsPerGene 

minCountsPerCell 

BPPARAM 

...  Additional arguments. 
Varies depending on the caller
argument:
caller = "edgeR"
: DEGLRT
.
caller = "DESeq2"
: Unshrunken DESeqResults
.
Apply DESeq2::lfcShrink()
if shrunken results are desired.
Perform pairwise differential expression across groups of cells. Currently supports edgeR and DESeq2 as DE callers.
We are no longer recommending the use of software that attempts to mitigate zero count inflation (e.g. zinbwave, zingeR) for UMI dropletbased single cell RNAseq data. Simply model the counts directly.
Updated 20190731.
We're providing preliminary support for DESeq2 as the differential expression caller. It is currently considerably slower for large datasets than edgeR.
We're trying to follow the conventions used in DESeq2 for contrasts, defining
the name of the factor in the design formula, numerator, and denominator
level for the fold change calculations. See DESeq2::results()
for details.
Van de Berge and Perraudeau and others have shown the LRT may perform better
for null hypothesis testing, so we use the LRT. In order to use the Wald
test, it is recommended to set useT = TRUE
(not currently in use).
For UMI data, for which the expected counts may be very low, the likelihood
ratio test implemented in nbinomLRT()
should be used.
Note that DESeq2 supports weights()
values automatically, if slotted using
zinbwave (which is no longer recommended for droplet scRNAseq).
The LRT has been shown to perform better for null hypothesis testing with
droplet scRNAseq data. Here we are using edgeR::glmLRT()
internally.
edgeR is currently significantly faster than DESeq2 for large datasets.
Note that Seurat currently uses the convention cells.1
for the numerator
and cells.2
for the denominator. See Seurat::FindMarkers()
for details.
data(Seurat, package = "acidtest") object < Seurat ## Compare expression in cluster 3 relative to 2. clusters < clusters(object) numerator < names(clusters)[clusters == "3"] summary(numerator)#> Length Class Mode #> 0 character character#> Length Class Mode #> 19 character character## edgeR ==== # x < diffExp( # object = object, # numerator = numerator, # denominator = denominator, # caller = "edgeR" # ) # class(x) # summary(x) ## DESeq2 ==== # This will warn about weights with the minimal example. # x < diffExp( # object = object, # numerator = numerator, # denominator = denominator, # caller = "DESeq2" # ) # class(x) # summary(x)