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

This vigettte demonstrates how to run fastMNN on Seurat objects. Parameters and commands are based off of the fastMNN help page. If you use fastMNN, please cite:

Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors

Laleh Haghverdi, Aaron T L Lun, Michael D Morgan & John C Marioni

Nature Biotechnology, 2018

doi: 10.1038/nbt.4091

Bioconductor: https://bioconductor.org/packages/release/bioc/html/batchelor.html

knitr::opts_chunk$set(
  tidy = TRUE,
  tidy.opts = list(width.cutoff = 95),
  message = FALSE,
  warning = FALSE
)
options(timeout = 1000)

Prerequisites to install:

• Seurat
• SeuratWrappers
• SeuratData

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

{.tabset .tabset-pills}

Systematic comparative analysis of human PBMC

To learn more about this dataset, type ?pbmcsca

InstallData("pbmcsca")
data("pbmcsca")
pbmcsca <- NormalizeData(pbmcsca)
pbmcsca <- FindVariableFeatures(pbmcsca)
pbmcsca <- RunFastMNN(object.list = SplitObject(pbmcsca, split.by = 'Method'))
pbmcsca <- RunUMAP(pbmcsca, reduction = 'mnn', dims = 1:30)
pbmcsca <- FindNeighbors(pbmcsca, reduction = 'mnn', dims = 1:30)
pbmcsca <- FindClusters(pbmcsca)
DimPlot(pbmcsca, group.by = c('Method', 'ident',"CellType"), ncol = 3)

rm(pbmcsca)
gc(verbose = FALSE)

Interferon-stimulated and control PBMC

To learn more about this dataset, type ?ifnb

InstallData("ifnb")
data("ifnb")
ifnb <- NormalizeData(ifnb)
ifnb <- FindVariableFeatures(ifnb)
ifnb <- RunFastMNN(object.list = SplitObject(ifnb, split.by = 'stim'))
ifnb <- RunUMAP(ifnb, reduction = 'mnn', dims = 1:30)
ifnb <- FindNeighbors(ifnb, reduction = 'mnn', dims = 1:30)
ifnb <- FindClusters(ifnb)
DimPlot(ifnb, group.by = c('stim', 'ident', 'seurat_annotations'), ncol = 3)

rm(ifnb)
gc(verbose = FALSE)

Eight human pancreatic islet datasets

To learn more about this dataset, type ?panc8

InstallData("panc8")
data("panc8")
panc8 <- NormalizeData(panc8)
panc8 <- FindVariableFeatures(panc8)
panc8 <- RunFastMNN(object.list = SplitObject(panc8, split.by = 'replicate')[c("celseq", "celseq2", "fluidigmc1", "smartseq2")])
panc8 <- RunUMAP(panc8, reduction = 'mnn', dims = 1:30)
panc8 <- FindNeighbors(panc8, reduction = 'mnn', dims = 1:30)
panc8 <- FindClusters(panc8)
DimPlot(panc8, group.by = c('replicate', 'ident', 'celltype'), ncol = 3) 

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