Seurat (version 5.0.3)

PrepSCTFindMarkers: Prepare object to run differential expression on SCT assay with multiple models

Description

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.

Usage

PrepSCTFindMarkers(object, assay = "SCT", verbose = TRUE)

Value

Returns a Seurat object with recorrected counts and data in the SCT assay.

Arguments

object

Seurat object with SCT assays

assay

Assay name where for SCT objects are stored; Default is 'SCT'

verbose

Print messages and progress

Progress Updates with <span class="pkg">progressr</span>

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")

Parallelization with <span class="pkg">future</span>

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")

Examples

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
)