Merge two Seurat objects
MergeSeurat(object1, object2, project = NULL, min.cells = 0,
min.genes = 0, is.expr = 0, do.normalize = TRUE, scale.factor = 10000,
do.scale = FALSE, do.center = FALSE, names.field = 1,
names.delim = "_", add.cell.id1 = NULL, add.cell.id2 = NULL)First Seurat object to merge
Second Seurat object to merge
Project name (string)
Include genes with detected expression in at least this many cells
Include cells where at least this many genes are detected
Expression threshold for 'detected' gene
Normalize the data after merging. Default is TRUE. If set, will perform the same normalization strategy as stored for the first object
If normalizing on the cell level, this sets the scale factor.
In object@scale.data, perform row-scaling (gene-based z-score). FALSE by default, so run ScaleData after merging.
In object@scale.data, perform row-centering (gene-based centering). FALSE by default
For the initial identity class for each cell, choose this field from the cell's column name
For the initial identity class for each cell, choose this delimiter from the cell's column name
String to be appended to the names of all cells in object1
String to be appended to the names of all cells in object2
Merged Seurat object
# NOT RUN {
# Split pbmc_small for this example
pbmc1 <- SubsetData(object = pbmc_small, cells.use = pbmc_small@cell.names[1:40])
pbmc1
pbmc2 <- SubsetData(object = pbmc_small, cells.use = pbmc_small@cell.names[41:80])
pbmc2
# Merge pbmc1 and pbmc2 into one Seurat object
pbmc_merged <- MergeSeurat(object1 = pbmc1, object2 = pbmc2)
pbmc_merged
# }
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