## ----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,]