CreateSeuratObject: Initialize and setup the Seurat object
In mayer-lab/SeuratForMayer2018: Seurat : R Toolkit for Single Cell Genomics

Initializes the Seurat object and some optional filtering

CreateSeuratObject(raw.data, project = "SeuratProject", min.cells = 0,
  min.genes = 0, is.expr = 0, normalization.method = NULL,
  scale.factor = 10000, do.scale = FALSE, do.center = FALSE,
  names.field = 1, names.delim = "_", meta.data = NULL, save.raw = TRUE)

`raw.data`	Raw input data
`project`	Project name (string)
`min.cells`	Include genes with detected expression in at least this many cells. Will subset the raw.data matrix as well. To reintroduce excluded genes, create a new object with a lower cutoff.
`min.genes`	Include cells where at least this many genes are detected.
`is.expr`	Expression threshold for 'detected' gene. For most datasets, particularly UMI datasets, will be set to 0 (default). If not, when initializing, this should be set to a level based on pre-normalized counts (i.e. require at least 5 counts to be treated as expresesd) All values less than this will be set to 0 (though maintained in object@raw.data).
`normalization.method`	Method for cell normalization. Default is no normalization. In this case, run NormalizeData later in the workflow. As a shortcut, you can specify a normalization method (i.e. LogNormalize) here directly.
`scale.factor`	If normalizing on the cell level, this sets the scale factor.
`do.scale`	In object@scale.data, perform row-scaling (gene-based z-score). FALSE by default. In this case, run ScaleData later in the workflow. As a shortcut, you can specify do.scale=T (and do.center=T) here.
`do.center`	In object@scale.data, perform row-centering (gene-based centering)
`names.field`	For the initial identity class for each cell, choose this field from the cell's column name
`names.delim`	For the initial identity class for each cell, choose this delimiter from the cell's column name
`meta.data`	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
`save.raw`	TRUE by default. If FALSE, do not save the unmodified data in object@raw.data which will save memory downstream for large datasets