IKAP: IKAP: Identifying K mAjor cell Population group in single...

In NHLBI-BCB/IKAP: IKAP: Identifying K mAjor cell Population group in single cell analysis

Description

IKAP identifies candidate set(s) of major cell groups using the single cell analysis R package Seurat by evaluating sets of possible cell groups generated using different parameters in Seurat SNN clustering (i.e. resulotion r and the number of top principal components (nPC)). The results (tables and plots) are saved in the output directory. A Seurat object is returned with all sets of evaluated cell groups saved in the metadata data frame.

Usage

IKAP(sobj, pcs = NA, pc.range = 20, k.max = NA, r.kmax.est = 1.5,
  out.dir = "./IKAP", scale.data = TRUE, confounders = c("nUMI",
  "percent.mito"), plot.decision.tree = TRUE, random.seed = 0)

Arguments

sobj	a Seurat object with cell expression normalized
pcs	the list of principal components used for clustering. default is NA (to be determined by IKAP; recommended)
pc.range	the range of nPCs. default is 20
k.max	the maximal number of clusters. default is NA (to be determined by IKAP; recommended)
r.kmax.est	resolution for IKAP determining k.max using Seurat SNN clustering. default is 1.5
out.dir	the path for output directory
scale.data	whether scale the data using Seurat::ScaleData. default is TRUE (recommended)
confounders	a vector of confounders that need to be regressed out in Seurat::ScaleData. default is c('nUMI','percent.mito') (see Seurat tutorial: https://satijalab.org/seurat/pbmc3k_tutorial.html)
plot.decision.tree	whether to plot decision trees that classify the cell groups. default is TRUE
random.seed	random seed

Examples

sobj.new <- IKAP(sobj, out.dir = "./IKAP")

saveRDS(sobj.new, file = "./IKAP/sobj.new.rds")

NHLBI-BCB/IKAP documentation built on March 21, 2020, 8:08 p.m.