setClusters: setClusters

In zhongmicai/bigSCale2_singleCell: A framework for the analysis of single cell data (RNA-seq and ATAC-seq) including clustering,phenotyping,pseudotime and inferring gene regulatory networks.

Description

Calculates the clusters of cell types.

Usage

setClusters(object, customClust = NA, classifier = NA,
  num.classifiers = NA, ...)

Arguments

object	object of the SingleCellExperiment class.
customClust	A numeric vector containg your custom cluster assignment, overrides all previous settings.
classifier	New option which allows to cluster the cells according to two or three genes given as input.
num.classifiers	How many markers should be used for each group. Check online tutorial for further help https://github.com/iaconogi/bigSCale2#classifier
method.treshold	By default method.treshold=0.5. Increasing(decreasing) it results in bigSCale2 partitioning in more(less) clusters.
plot.clusters	By default plot.clusters=FALSE. If plot.clusters=TRUE plots a dendrogram of the clusters while making the analysis.
cut.depth	By default not used. It overrides the internal decisions of bigSCale2 and forces it to cut the dendrogram at cut.depth (0-100 percent).

Value

SingleCellExperiment object with the clusters stored inside.

Examples

sce=setClusters(sce)
sce=setClusters(sce,classifier='Cd4','Cd8')

zhongmicai/bigSCale2_singleCell documentation built on Nov. 5, 2019, 1:26 p.m.