clusterCellsInternal-scRNAseq: clusterCellsInternal

In ilyessr/conclus: ScRNA-seq Workflow CONCLUS - From CONsensus CLUSters To A Meaningful CONCLUSion

clusterCellsInternal

Description

Returns consensus clusters by using hierarchical clustering on the similarity matrix of cells.

Usage

clusterCellsInternal(theObject, clusterNumber=NULL, deepSplit=4, cores=2,
                clusteringMethod="ward.D2")

Arguments

theObject	An Object of class scRNASeq for which the count matrix was normalized (see ?normaliseCountMatrix), tSNE were calculated (see ?generateTSNECoordinates), and dbScan was run (see ?runDBSCAN),
clusterNumber	Exact number of cluster. Default = NULL that will determine the number of clusters automatically.
deepSplit	Intuitive level of clustering depth. Options are 1, 2, 3, 4. Default = 4
cores	Maximum number of jobs that CONCLUS can run in parallel. Default is 1.
clusteringMethod	Clustering method passed to hclust() function. See ?hclust for a list of method. Default = "ward.D2".

Value

An object of class scRNASeq with its cellsSimilarityMatrix and sceNorm slots updated.

Author(s)

Ilyess RACHEDI, based on code by Polina PAVLOVICH and Nicolas DESCOSTES.

See Also

plotCellSimilarity

Examples

## Object containing the results of previous steps
load(system.file("extdata/scrFull.Rdat", package="conclus"))

## Compute the cell similarity matrix
scr <- clusterCellsInternal(scr, clusterNumber=4, cores=2)

ilyessr/conclus documentation built on April 8, 2022, 1:43 p.m.