unCTC_pathway_plots: Pathway based principal component analysis plots

In SaritaPoonia/unCTC: Characterising single circulating tumor cell transcriptomes

Pathway based principal component analysis plots

Description

unCTC UMAP and PCA plots

Usage

unCTC_pathway_plots(
  Pathway_score,
  Pathway_metadata,
  colorby = "Data_id",
  Color_cluster = "Clusters",
  pairsplotLegend = c("left", "right", "none")
)

Arguments

Pathway_score	Pathway score matrix. Row names are pathway/ genesets names and column names are samples/ cells
Pathway_metadata	PathwayDDLK_clust output from DDLK_Clust function
colorby	color by any column name from Pathway_metadata, if pathway metadata is not given then default is "Data_id"
Color_cluster	Any column name from Pathway_metadata, default is "Clusters"
pairsplotLegend	Legend position for pairsplot.

Value

plots list of color by class, color by clusters, pairsplot

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)

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

PathwayScore = DDLK_Clusters$Pathway_score
PathwayMetadata = DDLK_Clusters$PathwayDDLK_clust
Plots_output = unCTC_pathway_plots(Pathway_score=PathwayScore,
                           Pathway_metadata = PathwayMetadata,
                           colorby = "Data_id",
                           Color_cluster = "Clusters")

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