View source: R/differential_expression.R
PrepSCTFindMarkers | R Documentation |
Given a merged object with multiple SCT models, this function uses minimum of the median UMI (calculated using the raw UMI counts) of individual objects to reverse the individual SCT regression model using minimum of median UMI as the sequencing depth covariate. The counts slot of the SCT assay is replaced with recorrected counts and the data slot is replaced with log1p of recorrected counts.
PrepSCTFindMarkers(object, assay = "SCT", verbose = TRUE)
object |
Seurat object with SCT assays |
assay |
Assay name where for SCT objects are stored; Default is 'SCT' |
verbose |
Print messages and progress |
Returns a Seurat object with recorrected counts and data in the SCT assay.
This function uses
progressr to
render status updates and progress bars. To enable progress updates, wrap
the function call in with_progress
or run
handlers(global = TRUE)
before running
this function. For more details about progressr, please read
vignette("progressr-intro")
This function uses
future to enable
parallelization. Parallelization strategies can be set using
plan
. Common plans include “sequential
”
for non-parallelized processing or “multisession
” for parallel
evaluation using multiple R sessions; for other plans, see the
“Implemented evaluation strategies” section of
?future::plan
. For a more thorough introduction
to future, see
vignette("future-1-overview")
data("pbmc_small")
pbmc_small1 <- SCTransform(object = pbmc_small, variable.features.n = 20, vst.flavor="v1")
pbmc_small2 <- SCTransform(object = pbmc_small, variable.features.n = 20, vst.flavor="v1")
pbmc_merged <- merge(x = pbmc_small1, y = pbmc_small2)
pbmc_merged <- PrepSCTFindMarkers(object = pbmc_merged)
markers <- FindMarkers(
object = pbmc_merged,
ident.1 = "0",
ident.2 = "1",
assay = "SCT"
)
pbmc_subset <- subset(pbmc_merged, idents = c("0", "1"))
markers_subset <- FindMarkers(
object = pbmc_subset,
ident.1 = "0",
ident.2 = "1",
assay = "SCT",
recorrect_umi = FALSE
)
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