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Create a Seurat object from a feature (e.g. gene) expression matrix. The expected format of the input matrix is features x cells.
CreateSeuratObject(counts, project = "SeuratProject", assay = "RNA",
min.cells = 0, min.features = 0, names.field = 1,
names.delim = "_", meta.data = NULL)Unnormalized data such as raw counts or TPMs
Sets the project name for the Seurat object.
Name of the assay corresponding to the initial input data.
Include features detected in at least this many cells. Will subset the counts matrix as well. To reintroduce excluded features, create a new object with a lower cutoff.
Include cells where at least this many features are detected.
For the initial identity class for each cell, choose this field from the cell's name. E.g. If your cells are named as BARCODE_CLUSTER_CELLTYPE in the input matrix, set names.field to 3 to set the initial identities to CELLTYPE.
For the initial identity class for each cell, choose this delimiter from the cell's column name. E.g. If your cells are named as BARCODE-CLUSTER-CELLTYPE, set this to "-" to separate the cell name into its component parts for picking the relevant field.
Additional cell-level 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.
Note: In previous versions (<3.0), this function also accepted a parameter to set the expression threshold for a 'detected' feature (gene). This functionality has been removed to simplify the initialization process/assumptions. If you would still like to impose this threshold for your particular dataset, simply filter the input expression matrix before calling this function.
# NOT RUN {
pbmc_raw <- read.table(
file = system.file('extdata', 'pbmc_raw.txt', package = 'Seurat'),
as.is = TRUE
)
pbmc_small <- CreateSeuratObject(counts = pbmc_raw)
pbmc_small
# }
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