Compiled: June 23, 2021
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
Prerequisites to install:
library(Seurat)
library(SeuratData)
library(SeuratWrappers)
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)
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)
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)
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