In SydneyBioX/scDC: Single cell differential composition analysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Package

library(scDC)

Data

data("sim")
exprsMat <- sim$sim_exprsMat
subject <- sim$sim_subject
cellTypes <- sim$sim_cellTypes
cond <- sim$sim_cond

dim(exprsMat)
table(subject, cellTypes)
table(cond, cellTypes)

scDC (without clustering)

Set nboot = 50 for illustration.

res_scDC_noClust <- scDC_noClustering(cellTypes, subject, calCI = TRUE, 
                                     calCI_method = c("percentile", "BCa", "multinom"),
                                     nboot = 50)

Visualisation

barplotCI(res_scDC_noClust, c("cond1","cond1","cond1","cond1",
                              "cond2","cond2","cond2","cond2"))

densityCI(res_scDC_noClust, c("cond1","cond1","cond1","cond1",
                              "cond2","cond2","cond2","cond2"))

Fitting GLM

res_GLM <- fitGLM(res_scDC_noClust, c("cond1","cond1","cond1","cond1",
                                      "cond2","cond2","cond2","cond2"), 
                  pairwise = FALSE)

Summary GLM results

Fixed effect

summary(res_GLM$pool_res_fixed)

Random effect

summary(res_GLM$pool_res_random)

scDC (with clustering) (Not Run)

res_scDC_clust = scDC_noClustering(cellTypes, 
                                   subject, calCI = TRUE, 
                                   calCI_method = c("percentile", "BCa", "multinom"))

Visualisation

barplotCI(res_scDC_clust, c("cond1","cond1","cond1","cond1",
                            "cond2","cond2","cond2","cond2")))
densityCI(res_scDC_clust, c("cond1","cond1","cond1","cond1",
                            "cond2","cond2","cond2","cond2")))

Fitting GLM

res_GLM <- fitGLM(res_scDC_noClust,
                  c("cond1","cond1","cond1","cond1",
                    "cond2","cond2","cond2","cond2"), pairwise = FALSE)

Summary GLM results

Fixed effect

summary(res_GLM$pool_res_fixed)

Random effect

summary(res_GLM$pool_res_random)

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