DOWNLOAD

drugs and affected lncRNAs according to drug names (|log2 FC| ≤ 2):   Drug2Fold-db.txt

drugs and affected lncRNAs according to drug names (|log2 FC| ≤ 1.5):   Drug1.5Fold-db.txt

drugs and affected lncRNAs according to instance ID (|log2 FC| ≤ 2):   Instance2Fold-db.txt

drugs and affected lncRNAs according to instance ID (|log2 FC| ≤ 1.5):   Instance1.5Fold-db.txt

lncRNAs in LNCmap:   lncRNAs_LNCmap.xls

drugs in LNCmap:   drugs_LNCmap.xls

drug induce-lncRNA sets:   drug-induced_lncRNASets.xls

How to use LSEA and ORA in local machine:

LSEA and ORA algorithms were based on R script. LSEA algorithm was implemented by using core code of R-GSEA script, and ORA algorithm was implemented using a hypergeometric test in R script. Before you implement your enrichment analysis, please download dataset.zip file and decompress it in your local machine. The available resource of LSEA and ORA can be downloaded as follows:

R-GSEA:   http://software.broadinstitute.org/gsea/downloads.jsp

ORA script:   ORA

Dataset used in enrichment analysis:   dataset

LncRNAProfile example for LSEA:   LncRNAProfile.gct

LncRNAProfileLabel example for LSEA:   LncRNAProfileLabel.cls

lncRNA list example fro ORA:   lncRNA_list.txt

###########Example code for LSEA##############

source("GSEA.1.0.R", verbose=T, max.deparse.length=9999)

GSEA(                                                                       
 input.ds =  "LncRNAProfile.gct",                # Input lncRNA expression Affy dataset file in GCT format
 input.cls = "LncRNAProfileLabel.cls",           # Input class vector (phenotype) file in CLS format
 gs.db =     "./dataset/drugName2fold.gmt",                # lncRNA set database in GMT format
 output.directory      = "./result/LNCMap/",      # Directory where to store output and results (default: "")

 doc.string            = "LNCMap",        
 non.interactive.run   = F,               
 reshuffling.type      = "sample.labels", 
 nperm                 = 1000,            
 weighted.score.type   =  1,             
 nom.p.val.threshold   = -1,             
 fwer.p.val.threshold  = -1,             
 fdr.q.val.threshold   = 0.25,            
 topgs                 = 20,              
 adjust.FDR.q.val      = F,               
 gs.size.threshold.min = 15,              
 gs.size.threshold.max = 500,             
 reverse.sign          = F,               
 preproc.type          = 0,               
 random.seed           = 760435,          
 perm.type             = 0,               
 fraction              = 1.0,             
 replace               = F,              
 save.intermediate.results = F,           
 OLD.GSEA              = F,               
 use.fast.enrichment.routine = T          
)

GSEA.Analyze.Sets(
   directory           = "./result/LNCMap/",        # Directory where to store output and results (default: "")
   topgs = 20,                                     
   height = 16,
   width = 16
)
		
		

###########Example code for ORA##############

source("ORA.R")
pth<-0.01      ##p-value threshold
lncRNA_list<-read.delim ("lncRNA_list.txt",header=T)
allLncRNAs<-read.delim("./dataset/gencodeV19Lnc.txt",header=T)
hypers<-intersect(lncRNA_list[,1],allLncRNAs[,1])
dataset<- read.delim("./dataset/drugName2fold.txt", header=F, stringsAsFactors = F, sep=";")
names(hypers) <- c("hyper")
names(dataset) <- c("DrugName", "ATCCode", "lncRNAs")
result<-LNCmapHyperAnalysis(data.frame(hypers),dataset,lncNum,pth)

		

Depends: R (≥ 2.15.2)