View source: R/GroupsDiffPathways.R
Differential_pathways | R Documentation |
Provide differential pathways between given groups.
Differential_pathways( Pathway_score, DDLK_Clusters, DifferentiateBy = "Clusters", p_val = 0.05, lfc = 0, up_pathways_number = 10 )
Pathway_score |
Output of PathwayEnrichmentScore.R method |
DDLK_Clusters |
Output of DDLK_Clust.R method |
DifferentiateBy |
Any column name from DDLK_Clusters$PathwayDDLK_clust, default is "Clusters" |
p_val |
Threshold p value, default is 0.05 |
lfc |
Threshold log fold change value, default is 0 |
up_pathways_number |
select number of upregulated pathways in each group |
Diff_Pathways list of three objects 1. DiffMatpathway = Pathway matrix with top most up_pathways_number differential pathways in each group 2. Diffup_pathways = top most up_pathways_number differential pathways in each group 3. annotations = Cell wise annotation of DifferentialMatrix
data1 = unCTC::Poonia_et_al._TPMData data2 = unCTC::Ding_et_al._WBC1_TPMData Data_list = list(data1,data2) Data_Id = list("data1","data2") Genesets = unCTC::c2.all.v7.2.symbols Pathway_score = PathwayEnrichmentScore(data_list=Data_list, data_id= Data_Id, min_Sample = 5, min_Gene = 1500, Genesets=Genesets, min.size=70, max.size=100) DDLK_Clusters = DDLK_Clust(PathwayScore = Pathway_score$Pathway_score, PathwayMetaData=Pathway_score$Pathway_metadata, n=3, out.dir = paste0(getwd(),"/unCTC")) Output = Differential_pathways(Pathway_score=Pathway_score, DDLK_Clusters=DDLK_Clusters, DifferentiateBy = "Clusters")
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