scDC_clustering: scDC_clustering

In SydneyBioX/scDC: Single cell differential composition analysis

scDC_clustering

Description

Single-cell Differential Composition Analysis with performing clustering

Usage

scDC_clustering(
  exprsMat = NULL,
  cellTypes = NULL,
  subject = NULL,
  calCI = TRUE,
  calCI_method = c("BCa", "multinom", "percentile"),
  nboot = NULL,
  conf_level = 0.95,
  ncores = 1,
  verbose = TRUE
)

Arguments

exprsMat	logcounts expression matrix with each row represents gene, and each column represents cell
cellTypes	A vector indicates the cell type info of the data
subject	A vector indicates the subject info of the data
calCI	A logical input for whether calculating the confidence interval for proportion
calCI_method	A string indicates the method that is used to calculate confidence interval. Options include BCa, percentile, and multinom.
nboot	Number of bootstrap. If calCI = TRUE, nboot = 10000 by default. Otherwise, nboot = 500.
conf_level	confidence level, with default 0.95
ncores	Number of cores that are used.
verbose	A logical input for whether print the progress.

Value

Returns a data frame.

Author(s)

Yingxin Lin

Examples

## Loading example data
library(scDC)
data("sim")

cellTypes = sim$sim_cellTypes
subject = sim$sim_subject
## Not run: 
res_noCALCI = scDC_clustering(cellTypes, subject, calCI = FALSE)
res_BCa = scDC_clustering(cellTypes, subject, calCI = TRUE, calCI_method = "BCa")
res_percentile = scDC_clustering(cellTypes, subject, calCI = TRUE, calCI_method = "percentile")
res_multinom = scDC_clustering(cellTypes, subject, calCI = TRUE, calCI_method = "multinom")


## End(Not run)

SydneyBioX/scDC documentation built on Nov. 5, 2023, 2:10 a.m.