In satijalab/seurat-wrappers: Community-Provided Methods and Extensions for the Seurat Object

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title: "Fast Differential Expression with Presto" date: 'Compiled: r format(Sys.Date(), "%B %d, %Y")' output: github_document: html_preview: true toc: true toc_depth: 3 fig_width: 16 html_document: df_print: kable theme: united fig_height: 5 fig_width: 16 out_height: 4

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This vignette demonstrates the use of the Presto package in Seurat. Commands and parameters are based off of the Presto tutorial. If you use Presto in your work, please cite:

Presto scales Wilcoxon and auROC analyses to millions of observations

Ilya Korsunsky, Aparna Nathan, Nghia Millard, Soumya Raychaudhuri

bioRxiv, 2019.

Pre-print: https://www.biorxiv.org/content/10.1101/653253v1.full.pdf

GitHub: https://github.com/immunogenomics/presto

if (!requireNamespace("presto")) {
  remotes::install_github("immunogenomics/presto", upgrade = FALSE)
}
knitr::opts_chunk$set(
  tidy = TRUE,
  tidy.opts = list(width.cutoff = 95),
  message = FALSE,
  warning = FALSE
)

Prerequisites to install:

• Seurat
• Presto
• SeuratWrappers
• SeuratData

library(presto)
library(Seurat)
library(SeuratData)
library(SeuratWrappers)

Differential Expression Testing for PBMC scRNA-seq Data

To learn more about this dataset, type ?pbmc3k

InstallData("pbmc3k")
data("pbmc3k")
pbmc3k <- NormalizeData(pbmc3k)
Idents(pbmc3k) <- 'seurat_annotations'

diffexp.B.Mono <- RunPresto(pbmc3k, 'CD14+ Mono', 'B')
head(diffexp.B.Mono, 10)

diffexp.all <- RunPrestoAll(pbmc3k)
head(diffexp.all[diffexp.all$cluster=='B', ], 10)

satijalab/seurat-wrappers documentation built on April 10, 2024, 3:25 p.m.