createCellChat: Create a new CellChat object from a data matrix, Seurat or...

In sqjin/CellChat: Inference and analysis of cell-cell communication from single-cell and spatial transcriptomics data

Create a new CellChat object from a data matrix, Seurat or SingleCellExperiment object

Description

Create a new CellChat object from a data matrix, Seurat or SingleCellExperiment object

Usage

createCellChat(
  object,
  meta = NULL,
  group.by = NULL,
  datatype = c("RNA", "spatial"),
  coordinates = NULL,
  scale.factors = NULL,
  assay = NULL,
  do.sparse = T
)

Arguments

object	a normalized (NOT count) data matrix (genes by cells), Seurat or SingleCellExperiment object
meta	a data frame (rows are cells with rownames) consisting of cell information, which will be used for defining cell groups. If input is a Seurat or SingleCellExperiment object, the meta data in the object will be used
group.by	a char name of the variable in meta data, defining cell groups. If input is a data matrix and group.by is NULL, the input ‘meta' should contain a column named ’labels', If input is a Seurat or SingleCellExperiment object, USER must provide 'group.by' to define the cell groups. e.g, group.by = "ident" for Seurat object
datatype	By default datatype = "RNA"; when running CellChat on spatial imaging data, set datatype = "spatial" and input 'scale.factors'
coordinates	a data matrix in which each row gives the spatial locations/coordinates of each cell/spot
scale.factors	a list containing the scale factors and spot diameter for the full/high/low resolution images. USER must input this list when datatype = "spatial". scale.factors must contain an element named 'spot.diameter', which is the theoretical spot size; e.g., 10x Visium (spot.size = 65 microns), and another element named 'spot', which is the number of pixels that span the diameter of a theoretical spot size in the original, full-resolution image. For 10X visium, scale.factors are in the 'scalefactors_json.json'. scale.factors$spot is the 'spot.size.fullres '
assay	Assay to use when the input is a Seurat object. NB: The data in the 'integrated' assay is not suitable for CellChat analysis because it contains negative values.
do.sparse	whether use sparse format

Examples

## Not run: 
Create a CellChat object from single-cell transcriptomics data
# Input is a data matrix
## create a dataframe consisting of the cell labels
meta = data.frame(labels = cell.labels, row.names = names(cell.labels))
cellChat <- createCellChat(object = data.input, meta = meta, group.by = "labels")

# input is a Seurat object
## use the default cell identities of Seurat object
cellChat <- createCellChat(object = seurat.obj, group.by = "ident", assay = "RNA")
## use other meta information as cell groups
cellChat <- createCellChat(object = seurat.obj, group.by = "seurat.clusters")

# input is a SingleCellExperiment object
cellChat <- createCellChat(object = sce.obj, group.by = "sce.clusters")

Create a CellChat object from spatial imaging data
# Input is a data matrix
cellChat <- createCellChat(object = data.input, meta = meta, group.by = "labels",
                           datatype = "spatial", coordinates = coordinates, scale.factors = scale.factors)

# input is a Seurat object
cellChat <- createCellChat(object = seurat.obj, group.by = "ident", assay = "SCT",
                           datatype = "spatial", scale.factors = scale.factors)


## End(Not run)

sqjin/CellChat documentation built on Nov. 10, 2023, 4:29 a.m.