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Add samples into existing Seurat object.
AddSamples(object, new.data, 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 = "_", meta.data = NULL, add.cell.id = NULL)
Seurat object
Data matrix for samples to be added
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 in the object
scale factor in the log normalization
In object@scale.data, perform row-scaling (gene-based z-score)
In object@scale.data, perform row-centering (gene-based centering)
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
Additional metadata to add to the Seurat object. Should be a data frame where the rows are cell names, and the columns are additional metadata fields
String to be appended to the names of all cells in new.data. E.g. if add.cell.id = "rep1", "cell1" becomes "cell1.rep1"
# NOT RUN {
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_data <- pbmc2@data
dim(pbmc2_data)
pbmc_added <- AddSamples(object = pbmc1, new.data = pbmc2_data)
pbmc_added
# }
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