celltrek: The core function of CellTrek

In navinlabcode/CellTrek: Spatial Charting of Single Cell Transcriptomes in Tissues

The core function of CellTrek

Description

The core function of CellTrek

Usage

celltrek(
  st_sc_int,
  int_assay = "traint",
  sc_data = NULL,
  sc_assay = "RNA",
  reduction = "pca",
  intp = T,
  intp_pnt = 10000,
  intp_lin = F,
  nPCs = 30,
  ntree = 1000,
  dist_thresh = 0.4,
  top_spot = 10,
  spot_n = 10,
  repel_r = 5,
  repel_iter = 10,
  keep_model = F,
  ...
)

Arguments

st_sc_int	Seurat traint object
int_assay	Integration assay ('traint')
sc_data	SC data, optional
sc_assay	SC assay
reduction	Dimension reduction method, usually 'pca'
intp	If True, do interpolation
intp_pnt	Number of interpolation points
intp_lin	If Ture, do linear interpolation
nPCs	Number of PCs
ntree	Number of Trees
dist_thresh	Distance threshold
top_spot	Maximum number of spots that one cell can be charted
spot_n	Maximum number of cells that one spot can contain
repel_r	Repelling radius
repel_iter	Repelling iterations
keep_model	If TRUE, return the trained random forest model
...

Value

Seurat object

Examples

celltrek_res <- celltrek(st_sc_int, int_assay='traint', sc_data=NULL, sc_assay='RNA', reduction='pca', intp=T, intp_pnt=10000, intp_lin=F, nPCs=30, ntree=1000, dist_thresh=.4, top_spot=10, spot_n=10, r=NULL, keep_model=F, ...)

navinlabcode/CellTrek documentation built on April 15, 2022, 8:04 a.m.