## ----eval=FALSE---------------------------------------------------------------
#  
#  library(miRLAB)
#  
#  dataset=system.file("extdata", "EMT35.csv", package="miRLAB")
#  cause=1:35 #column 1:35 are miRNAs
#  effect=36:1189 #column 36:1189 are mRNAs
#  
#  #predict miRNA targets using Pearson correlation
#  pearson=Pearson(dataset, cause, effect)
#  
#  #predict miRNA targets using Mutual Information
#  mi=MI(dataset, cause, effect)
#  
#  #predict miRNA targets using causal inference
#  ida=IDA(dataset, cause, effect, "stable", 0.01)
#  
#  #predict miRNA targets using linear regression
#  lasso=Lasso(dataset, cause, effect)
#  

## ----eval=FALSE---------------------------------------------------------------
#  library(miRLAB)
#  #validate the results of the top100 targets of each miRNA predicted
#  #by the four methods
#  dataset=system.file("extdata", "ToyEMT.csv", package="miRLAB")
#  pearson=Pearson(dataset, 1:3, 4:18)
#  miR200aTop10=bRank(pearson, 3, 10, TRUE)
#  groundtruth=system.file("extdata", "Toygroundtruth.csv", package="miRLAB")
#  miR200aTop10Confirmed = Validation(miR200aTop10, groundtruth)

## ----eval=FALSE---------------------------------------------------------------
#  library(miRLAB)
#  #validate the results of the top100 targets of each miRNA predicted
#  #by the four methods
#  dataset=system.file("extdata", "ToyEMT.csv", package="miRLAB")
#  EMTresults=Pearson(dataset, 1:3, 4:18)
#  top10=Extopk(EMTresults, 10)
#  groundtruth=system.file("extdata", "Toygroundtruth.csv", package="miRLAB")
#  top10Confirmed = Validation(top10, groundtruth)

## ----eval=FALSE---------------------------------------------------------------
#  library(miRLAB)
#  dataset=system.file("extdata", "ToyEMT.csv", package="miRLAB")
#  dataset=Read(dataset)
#  dataset[1:5,1:7]

## ----eval=FALSE---------------------------------------------------------------
#  library(miRLAB)
#  groundtruth=system.file("extdata", "Toygroundtruth.csv", package="miRLAB")
#  groundtruth=Read(groundtruth)
#  groundtruth[1:5,]