Single-cell Differential Composition Analysis with performing clustering

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.

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") # }