DDLK_Clust: Cluster pathway mapped expression mtrix using Deep Dictionary...

In SaritaPoonia/unCTC: Characterising single circulating tumor cell transcriptomes

Cluster pathway mapped expression mtrix using Deep Dictionary Learning with K-means clustering cost

Description

clustering datasets with thousands of samples, and incorporating the K-means clustering cost into the deep dictionary learning (DDL) framework

Usage

DDLK_Clust(
  PathwayScore,
  PathwayMetaData,
  n,
  out.dir = getwd(),
  MetaData = list()
)

Arguments

PathwayScore	Pathway_score matrix form PathwayEnrichmentScore output
PathwayMetaData	Pathway_metadata form PathwayEnrichmentScore output
n	integer number of clusters for k means.
out.dir	= Output directory to write Pathwayscore. Default is current directory.
MetaData	Optional, List of metadata of expression matricies in same order in which expression matricies in data_list, Column number and names of all the MetaData in the list must be same

Value

Pathways_score_cluster Return: 1. Pathway_score = Cell wise pathway enrichment score matrix, Pathways in row and cells/samples in column. 2. PathwayDDLK_clust = sample/Cell wise DDLK cluster information.

Examples

data1 = unCTC::Poonia_et_al._TPMData
data2 = unCTC::Ding_et_al._WBC1_TPMData
Data_list = list(data1,data2)
Data_Id = list("data1","data2")
Genesets = unCTC::c2.all.v7.2.symbols
Pathway_score = PathwayEnrichmentScore(data_list=Data_list,
                                        data_id= Data_Id,
                                        Genesets=Genesets,
                                        min.size=70,
                                        max.size=100)

cluster_output = DDLK_Clust(PathwayScore = Pathway_score$Pathway_score,
                           PathwayMetaData=Pathway_score$Pathway_metadata,
                            n=3,
                            out.dir = paste0(getwd(),"/unCTC"))

SaritaPoonia/unCTC documentation built on Nov. 8, 2022, 12:07 p.m.