inst/doc/vignette.R
In BIMSBbioinfo/reg2gene: Predicting the target genes for regulatory elements
## ----global_options, include=FALSE---------------------------------------
library(knitr)
library(reg2gene)
library(InteractionSet)
library(GenomicRanges)
library(rmarkdown)
opts_chunk$set(warning = FALSE,
message= FALSE,
fig.align='center',
fig.path='Figures',
dev='png',
fig.show='hold',
cache=FALSE)
## ----fig2, fig.height = 5, fig.width = 3, fig.align = "center"-----------
#knitr::include_graphics("https://github.com/BIMSBbioinfo/reg2gene/blob/master/vignettes/Figures/QuantificationDataIntegrationSimplified.png")
knitr::include_graphics("/data/akalin/Projects/AAkalin_reg2gene/reg2gene/vignettes/Figures/QuantificationDataIntegrationSimplified.png")
## ------------------------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",2),"chr2",rep("chr1",3)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3,5,5,5)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3,5,5,5))
bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw))
## ----echo=FALSE----------------------------------------------------------
require(InteractionSet)
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",2),"chr2",rep("chr1",3)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3,5,5,5)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",c(1,1,3,5,5,5))
# if exons input is of GInteractions class object, the same output is obtained
exons= GInteractions(regTSS_toy,regTSS_toy$reg)
exons$name=regTSS_toy$name
exons$name2=regTSS_toy$name2
exons
print("Which results in:")
bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw))
## ------------------------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",4),rep("chr2",2)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3:6)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3:length(regTSS_toy)))
regActivity(regTSS_toy,c(test.bw,test2.bw))
## ------------------------------------------------------------------------
regTSS_toy <- GRReg1_toy
regTSS_toy$bw1 <- rep(1,length(GRReg1_toy))
regTSS_toy$bw2 <- rep(2,length(GRReg1_toy))
regTSS_toy$bw3 <- rep(3,length(GRReg1_toy))
regReg_toy <- GRReg2_toy
regReg_toy$bw1 <- rep(3,length(regReg_toy))
regReg_toy$bw2 <- rep(4,length(regReg_toy))
regActivityAroundTSS(regReg_toy,regTSS_toy,upstream=1,downstream=1)
## ---- echo=FALSE---------------------------------------------------------
###############################
#STEP 1. Getting random and predefined .8 correlation
require(GenomicRanges)
require(doMC)
require(glmnet)
require(foreach)
require(stringr)
require(qvalue)
####################################
# create example
x <- c(2.000346,2.166255,0.7372374,0.9380581,2.423209,
2.599857,4.216959,2.589133,1.848172,3.039659)
y <- c(2.866875,2.817145,2.1434456,2.9039771,3.819091,5.009990,
5.048476,2.884551,2.780067,4.053136)
corrM <- rbind(x,y)
# define Granges object
gr0 <- GRanges(seqnames=rep("chr1",2),IRanges(1:2,3:4))
GeneInfo <- as.data.frame(matrix(rep(c("gene","regulatory"),each=3),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr0) <- DataFrame(cbind(GeneInfo,corrM))
gr0
## ---- echo=FALSE---------------------------------------------------------
###############################
#STEP 1. Getting random and predefined .8 correlation
require(GenomicRanges)
require(doMC)
require(glmnet)
require(foreach)
require(stringr)
require(qvalue)
####################################
# create example
x <- c(2.000346,2.166255,0.7372374,0.9380581,2.423209,
2.599857,4.216959,2.589133,1.848172,3.039659)
y <- c(2.866875,2.817145,2.1434456,2.9039771,3.819091,5.009990,
5.048476,2.884551,2.780067,4.053136)
corrM <- rbind(x,y)
# define Granges object
gr0 <- GRanges(seqnames=rep("chr1",2),IRanges(1:2,3:4))
GeneInfo <- as.data.frame(matrix(rep(c("gene","regulatory"),each=3),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr0) <- DataFrame(cbind(GeneInfo,corrM))
print("associateReg2Gene(gr0,cores = 1,B=100)")
associateReg2Gene(gr0,cores = 1,B=100)
## ----fig3, fig.height = 5, fig.width = 5, fig.align = "center"-----------
#knitr::include_graphics("https://github.com/BIMSBbioinfo/reg2gene/master/vignettes/Figures/VOTING.png")
knitr::include_graphics("/data/akalin/Projects/AAkalin_reg2gene/reg2gene/vignettes/Figures/VOTING.png")
## ---- echo=FALSE---------------------------------------------------------
require(GenomicRanges)
require(InteractionSet)
gr2 <- gr <- GRanges(seqnames=rep("chr1",3),IRanges(1:3,3:5))
x <- 1:5
y <- 2:6
z <- 10:14
a <- rep(0,length(x))
GeneInfo <- as.data.frame(matrix(c(rep("gene",3),rep("regulatory",6)),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr) <- DataFrame(cbind(GeneInfo,rbind(x,y,z)))
mcols(gr2) <- DataFrame(cbind(GeneInfo,rbind(x,y,a)))
# create associateReg2Gene output objects, GInteractions will all
# output results
AssocObject <- reg2gene::associateReg2Gene(gr)
AssocObject2 <- reg2gene::associateReg2Gene(gr2)
# input for meta-analysis is list of such objects
associations <- list(AssocObject,AssocObject2)
names(associations) <- c("H3K4me1","H327ac")
# Run voteAssociations
voteAssociations(associations,
cutoff.stat="pval",
cutoff.val=0.05,
vote.threshold=0.5)
## ---- fig.height = 10, fig.width = 10, fig.align = "center"--------------
#knitr::include_graphics("https://github.com/BIMSBbioinfo/reg2gene/blob/master/vignettes/Figures/Meta-Analysis_Simplified.png")
knitr::include_graphics("/data/akalin/Projects/AAkalin_reg2gene/reg2gene/vignettes/Figures/Meta-Analysis_Simplified.png")
## ---- echo=FALSE---------------------------------------------------------
# creating datasets
require(GenomicRanges)
require(InteractionSet)
gr2 <- gr <- GRanges(seqnames=rep("chr1",3),IRanges(1:3,3:5))
x <- 1:5
y <- 2:6
z <- 10:14
a <- rep(0,length(x))
GeneInfo <- as.data.frame(matrix(c(rep("gene",3),rep("regulatory",6)),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr) <- DataFrame(cbind(GeneInfo,rbind(x,y,z)))
mcols(gr2) <- DataFrame(cbind(GeneInfo,rbind(x,y,a)))
# create associateReg2Gene output objects, GInteractions will all
# output results
AssocObject <- reg2gene::associateReg2Gene(gr)
AssocObject2 <- reg2gene::associateReg2Gene(gr2)
# input for meta-analysis is list of such objects
associations <- list(AssocObject,AssocObject2)
names(associations) <- c("Roadmap","Blueprint")
# Run metaA
metaAssociations(associations)
## ----fig5, fig.height = 5, fig.width = 3, fig.align = "center"-----------
#knitr::include_graphics("https://github.com/BIMSBbioinfo/reg2gene/blob/master/vignettes/Figures/BenchSimpleE.png")
knitr::include_graphics("/data/akalin/Projects/AAkalin_reg2gene/reg2gene/vignettes/Figures/BenchSimpleE.png")
## ------------------------------------------------------------------------
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[2]
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)
# removing confusing meta-data
mcols(reg2Gene) <- NULL
benchmarkAssociations(reg2Gene,
benchData,
binary=TRUE)
## ---- echo=FALSE---------------------------------------------------------
tmp <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[2]
mcols(tmp) <- NULL
tmp
## ---- echo=FALSE---------------------------------------------------------
tmp <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[5]
mcols(tmp) <- NULL
tmp
## ---- echo=FALSE---------------------------------------------------------
reg2GeneBench <- GInteractions(GRReg1_toy,GRReg1_toy$reg)
Bench <- reg2GeneBench$anchor1.Bench1Exp
Filter <- reg2GeneBench$anchor1.Filter1Exp
mcols(reg2GeneBench) <- NULL
reg2GeneBench$Pval <- seq(0, 1, length.out = length(GRReg1_toy))
reg2GeneBench$Bench <- Bench
reg2GeneBench$Filter <- Filter
reg2GeneBench
## ---- echo=FALSE---------------------------------------------------------
reg2GeneBench <- GInteractions(GRReg1_toy,GRReg1_toy$reg)
Bench <- reg2GeneBench$anchor1.Bench1Exp
Filter <- reg2GeneBench$anchor1.Filter1Exp
mcols(reg2GeneBench) <- NULL
reg2GeneBench$Pval <- seq(0, 1, length.out = length(GRReg1_toy))
reg2GeneBench$Bench <- Bench
reg2GeneBench$Filter <- Filter
confusionMatrix(reg2GeneBench,
thresholdID = "Pval",
thresholdValue = 0.05,
benchCol = "Bench",
prefilterCol = "Filter",
statistics = "ConfusionMatrix")
## ------------------------------------------------------------------------
genomicRegions <- GRanges(c("chr1:1-2", # 1. overlap prom
"chr2:1-2", # 2. overlap enh
"chr3:1-2", # 3. overlap tss +/- 1,000,000
"chr4:1-2")) # 4. do not overlap tss +/- 1,000,000
# CREATE GInteractions test object
annotationsEnh <- GRanges(c("chr1:1-2",
"chr2:1-2",
"chr3:100000-100002",
"chr4:10000001-10000002"))
annotationsGenes <- GRanges(c("chr1:1-2",
"chr2:100000-100002",
"chr3:99999-100002",
"chr4:10000001-10000002"))
annotationsGenes$name=c("gen1","gen2","gen3","gen4")
seqlengths(annotationsEnh) <- seqlengths(annotationsGenes) <- rep(10000002,4)
annotations = GInteractions(annotationsEnh,annotationsGenes)
annotateGenomicRegions(genomicRegions,
annotations =annotations,
identified=T)
## ------------------------------------------------------------------------
annotateGenomicRegions(genomicRegions,
annotations,
identified=F)
## ---- echo=FALSE---------------------------------------------------------
reg2gene::GRReg1_toy
## ------------------------------------------------------------------------
reg2gene::GRReg2_toy
## ------------------------------------------------------------------------
reg2gene::GRReg1_toy[7]
## ------------------------------------------------------------------------
GRReg2_toy[10:12]
## ------------------------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regActivityInputExample <- c(test.bw,test2.bw)
regActivityInputExample
## ------------------------------------------------------------------------
GRReg1_toyGI <- GRReg1_toy
GRReg1_toyGI <- GInteractions(GRReg1_toyGI,GRReg1_toyGI$reg)
GRReg1_toyGI[1:3]
## ----echo=FALSE----------------------------------------------------------
require(InteractionSet)
GRReg1_toyGI <- reg2gene::GRReg1_toy[1]
GRReg1_toyGI <- GInteractions(GRReg1_toyGI,GRReg1_toyGI$reg)
mcols(GRReg1_toyGI) <- mcols(GRReg1_toyGI)[1:3]
GRReg1_toyGI
## ----echo=FALSE----------------------------------------------------------
toy <- reg2gene::GRReg1_toy[1]
mcols(toy) <- mcols(toy)[1:3]
toy
## ------------------------------------------------------------------------
require(reg2gene)
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regActivityInputExample <- c(test.bw,test2.bw)
regActivityInputExample
## ------------------------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",2),"chr2",rep("chr1",3)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3,5,5,5)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3,5,5,5))
bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw),
sampleIDs=c("CellType1","CellType2"))
## ------------------------------------------------------------------------
sampleIDs <- c("/Reverse1.bw","/Forward1.bw","/Reverse2.bw","/Forward2.bw",
"Unstranded1")
sampleIDs
## ------------------------------------------------------------------------
libStrand <- c("+","-","+","-","*")
libStrand
## ----echo=FALSE----------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",2),"chr2",rep("chr1",3)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3,5,5,5)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3,5,5,5))
print("bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw),
normalize=\"quantile\")")
bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw),
normalize="quantile")
## ----echo=FALSE----------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",2),"chr2",rep("chr1",3)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3,5,5,5)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3,5,5,5))
print("bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw),
normalize=\"ratio\")")
bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw),
normalize="ratio")
## ----echo=FALSE----------------------------------------------------------
regTSS_toy <- GRanges(c(rep("chr1",4),rep("chr2",2)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3:6)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3:length(regTSS_toy)))
regTSS_toy
## ---- echo=FALSE---------------------------------------------------------
regTSS_toy <- GRReg1_toy
regTSS_toy$bw1 <- rep(1,length(GRReg1_toy))
regTSS_toy$bw2 <- rep(2,length(GRReg1_toy))
regTSS_toy$bw3 <- rep(3,length(GRReg1_toy))
regReg_toy <- GRReg2_toy
regReg_toy$bw1 <- rep(3,length(regReg_toy))
regReg_toy$bw2 <- rep(4,length(regReg_toy))
print("Result of upstream=5,downstream=5")
regActivityAroundTSS(regReg_toy,regTSS_toy,upstream=5,downstream=5)[1]
## ---- echo=FALSE---------------------------------------------------------
###############################
#STEP 1. Getting random and predefined .8 correlation
require(GenomicRanges)
require(doMC)
require(glmnet)
require(foreach)
require(stringr)
require(qvalue)
####################################
# create example
x <- c(2.000346,2.166255,0.7372374,0.9380581,2.423209,
2.599857,4.216959,2.589133,1.848172,3.039659)
y <- c(2.866875,2.817145,2.1434456,2.9039771,3.819091,5.009990,
5.048476,2.884551,2.780067,4.053136)
corrM <- rbind(x,y)
# define Granges object
gr0 <- GRanges(seqnames=rep("chr1",2),IRanges(1:2,3:4))
GeneInfo <- as.data.frame(matrix(rep(c("gene","regulatory"),each=3),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr0) <- DataFrame(cbind(GeneInfo,corrM))
print("associateReg2Gene(gr0,cores = 1,B=100)")
associateReg2Gene(gr0,cores = 1,B=100,asGInteractions=FALSE)
## ---- echo=FALSE---------------------------------------------------------
require(GenomicRanges)
require(InteractionSet)
gr2 <- gr <- GRanges(seqnames=rep("chr1",3),IRanges(1:3,3:5))
x <- 1:5
y <- 2:6
z <- 10:14
a <- rep(0,length(x))
GeneInfo <- as.data.frame(matrix(c(rep("gene",3),rep("regulatory",6)),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr) <- DataFrame(cbind(GeneInfo,rbind(x,y,z)))
mcols(gr2) <- DataFrame(cbind(GeneInfo,rbind(x,y,a)))
# create associateReg2Gene output objects, GInteractions will all
# output results
AssocObject <- reg2gene::associateReg2Gene(gr)
AssocObject2 <- reg2gene::associateReg2Gene(gr2)
# input for meta-analysis is list of such objects
associations <- list(AssocObject,AssocObject2)
names(associations) <- c("H3K4me1","H327ac")
associations
## ---- echo=FALSE---------------------------------------------------------
require(GenomicRanges)
require(InteractionSet)
gr2 <- gr <- GRanges(seqnames=rep("chr1",3),IRanges(1:3,3:5))
x <- 1:5
y <- 2:6
z <- 10:14
a <- rep(0,length(x))
GeneInfo <- as.data.frame(matrix(c(rep("gene",3),rep("regulatory",6)),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr) <- DataFrame(cbind(GeneInfo,rbind(x,y,z)))
mcols(gr2) <- DataFrame(cbind(GeneInfo,rbind(x,y,a)))
# create associateReg2Gene output objects, GInteractions will all
# output results
AssocObject <- reg2gene::associateReg2Gene(gr)
AssocObject2 <- reg2gene::associateReg2Gene(gr2)
# input for meta-analysis is list of such objects
associations <- list(AssocObject,AssocObject2)
names(associations) <- c("H3K4me1","H327ac")
# Run voteAssociations
voteAssociations(associations,
cutoff.stat="pval",
cutoff.val=0.05,
vote.threshold=0.51)
## ---- echo=FALSE---------------------------------------------------------
# creating datasets
require(GenomicRanges)
require(InteractionSet)
gr2 <- gr <- GRanges(seqnames=rep("chr1",3),IRanges(1:3,3:5))
x <- 1:5
y <- 2:6
z <- 10:14
a <- rep(0,length(x))
GeneInfo <- as.data.frame(matrix(c(rep("gene",3),rep("regulatory",6)),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr) <- DataFrame(cbind(GeneInfo,rbind(x,y,z)))
mcols(gr2) <- DataFrame(cbind(GeneInfo,rbind(x,y,a)))
# create associateReg2Gene output objects, GInteractions will all
# output results
AssocObject <- reg2gene::associateReg2Gene(gr)
AssocObject2 <- reg2gene::associateReg2Gene(gr2)
# input for meta-analysis is list of such objects
associations <- list(AssocObject,AssocObject2)
names(associations) <- c("Roadmap","Blueprint")
# Run metaA
associations
## ---- echo=FALSE---------------------------------------------------------
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[6]
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)[6]
# removing confusing meta-data
mcols(reg2Gene) <- NULL
mcols(benchData) <- NULL
print("test set:")
reg2Gene
print("benchmark set:")
benchData
print("after benchmarking results in:")
benchmarkAssociations(reg2Gene,
benchData,
binary=TRUE)
## ---- echo=FALSE---------------------------------------------------------
tmp <- GInteractions(GRReg2_toy,GRReg2_toy$reg)[10:12]
mcols(tmp) <- NULL
tmp
## ---- echo=FALSE---------------------------------------------------------
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[7]
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)[10:12]
mcols(reg2Gene) <- NULL
bench <- benchmarkAssociations(reg2Gene,
benchData,
binary=FALSE)
bench
## ---- echo=FALSE---------------------------------------------------------
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[7]
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)[10:12]
# removing confusing meta-data
mcols(reg2Gene) <- NULL
mcols(benchData) <- NULL
reg2Gene
benchData
benchmarkAssociations(reg2Gene,
benchData,
binary=TRUE)
## ------------------------------------------------------------------------
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)
benchDataList <- list(benchData,reg2Gene)
names(benchDataList) <- c("benchData1","benchData2")
benchmarkAssociations(reg2Gene,
benchDataList,
ignore.strand=TRUE,
binary=FALSE,
nCores = 1)
## ---- echo=FALSE---------------------------------------------------------
reg2GeneBench <- GInteractions(GRReg1_toy,GRReg1_toy$reg)
Bench <- reg2GeneBench$anchor1.Bench1Exp
Filter <- reg2GeneBench$anchor1.Filter1Exp
mcols(reg2GeneBench) <- NULL
reg2GeneBench$Pval <- seq(0, 1, length.out = length(GRReg1_toy))
reg2GeneBench$Bench <- Bench
reg2GeneBench$Filter <- Filter
confusionMatrix(reg2GeneBench,
thresholdID = "Pval",
thresholdValue = 0.05,
benchCol = "Bench",
prefilterCol = "Filter",
statistics = "PPV")
## ---- echo=FALSE---------------------------------------------------------
sessionInfo()
BIMSBbioinfo/reg2gene documentation built on May 3, 2019, 6:42 p.m.
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## ----global_options, include=FALSE---------------------------------------
library(knitr)
library(reg2gene)
library(InteractionSet)
library(GenomicRanges)
library(rmarkdown)
opts_chunk$set(warning = FALSE,
message= FALSE,
fig.align='center',
fig.path='Figures',
dev='png',
fig.show='hold',
cache=FALSE)
## ----fig2, fig.height = 5, fig.width = 3, fig.align = "center"-----------
#knitr::include_graphics("https://github.com/BIMSBbioinfo/reg2gene/blob/master/vignettes/Figures/QuantificationDataIntegrationSimplified.png")
knitr::include_graphics("/data/akalin/Projects/AAkalin_reg2gene/reg2gene/vignettes/Figures/QuantificationDataIntegrationSimplified.png")
## ------------------------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",2),"chr2",rep("chr1",3)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3,5,5,5)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3,5,5,5))
bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw))
## ----echo=FALSE----------------------------------------------------------
require(InteractionSet)
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",2),"chr2",rep("chr1",3)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3,5,5,5)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",c(1,1,3,5,5,5))
# if exons input is of GInteractions class object, the same output is obtained
exons= GInteractions(regTSS_toy,regTSS_toy$reg)
exons$name=regTSS_toy$name
exons$name2=regTSS_toy$name2
exons
print("Which results in:")
bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw))
## ------------------------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",4),rep("chr2",2)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3:6)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3:length(regTSS_toy)))
regActivity(regTSS_toy,c(test.bw,test2.bw))
## ------------------------------------------------------------------------
regTSS_toy <- GRReg1_toy
regTSS_toy$bw1 <- rep(1,length(GRReg1_toy))
regTSS_toy$bw2 <- rep(2,length(GRReg1_toy))
regTSS_toy$bw3 <- rep(3,length(GRReg1_toy))
regReg_toy <- GRReg2_toy
regReg_toy$bw1 <- rep(3,length(regReg_toy))
regReg_toy$bw2 <- rep(4,length(regReg_toy))
regActivityAroundTSS(regReg_toy,regTSS_toy,upstream=1,downstream=1)
## ---- echo=FALSE---------------------------------------------------------
###############################
#STEP 1. Getting random and predefined .8 correlation
require(GenomicRanges)
require(doMC)
require(glmnet)
require(foreach)
require(stringr)
require(qvalue)
####################################
# create example
x <- c(2.000346,2.166255,0.7372374,0.9380581,2.423209,
2.599857,4.216959,2.589133,1.848172,3.039659)
y <- c(2.866875,2.817145,2.1434456,2.9039771,3.819091,5.009990,
5.048476,2.884551,2.780067,4.053136)
corrM <- rbind(x,y)
# define Granges object
gr0 <- GRanges(seqnames=rep("chr1",2),IRanges(1:2,3:4))
GeneInfo <- as.data.frame(matrix(rep(c("gene","regulatory"),each=3),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr0) <- DataFrame(cbind(GeneInfo,corrM))
gr0
## ---- echo=FALSE---------------------------------------------------------
###############################
#STEP 1. Getting random and predefined .8 correlation
require(GenomicRanges)
require(doMC)
require(glmnet)
require(foreach)
require(stringr)
require(qvalue)
####################################
# create example
x <- c(2.000346,2.166255,0.7372374,0.9380581,2.423209,
2.599857,4.216959,2.589133,1.848172,3.039659)
y <- c(2.866875,2.817145,2.1434456,2.9039771,3.819091,5.009990,
5.048476,2.884551,2.780067,4.053136)
corrM <- rbind(x,y)
# define Granges object
gr0 <- GRanges(seqnames=rep("chr1",2),IRanges(1:2,3:4))
GeneInfo <- as.data.frame(matrix(rep(c("gene","regulatory"),each=3),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr0) <- DataFrame(cbind(GeneInfo,corrM))
print("associateReg2Gene(gr0,cores = 1,B=100)")
associateReg2Gene(gr0,cores = 1,B=100)
## ----fig3, fig.height = 5, fig.width = 5, fig.align = "center"-----------
#knitr::include_graphics("https://github.com/BIMSBbioinfo/reg2gene/master/vignettes/Figures/VOTING.png")
knitr::include_graphics("/data/akalin/Projects/AAkalin_reg2gene/reg2gene/vignettes/Figures/VOTING.png")
## ---- echo=FALSE---------------------------------------------------------
require(GenomicRanges)
require(InteractionSet)
gr2 <- gr <- GRanges(seqnames=rep("chr1",3),IRanges(1:3,3:5))
x <- 1:5
y <- 2:6
z <- 10:14
a <- rep(0,length(x))
GeneInfo <- as.data.frame(matrix(c(rep("gene",3),rep("regulatory",6)),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr) <- DataFrame(cbind(GeneInfo,rbind(x,y,z)))
mcols(gr2) <- DataFrame(cbind(GeneInfo,rbind(x,y,a)))
# create associateReg2Gene output objects, GInteractions will all
# output results
AssocObject <- reg2gene::associateReg2Gene(gr)
AssocObject2 <- reg2gene::associateReg2Gene(gr2)
# input for meta-analysis is list of such objects
associations <- list(AssocObject,AssocObject2)
names(associations) <- c("H3K4me1","H327ac")
# Run voteAssociations
voteAssociations(associations,
cutoff.stat="pval",
cutoff.val=0.05,
vote.threshold=0.5)
## ---- fig.height = 10, fig.width = 10, fig.align = "center"--------------
#knitr::include_graphics("https://github.com/BIMSBbioinfo/reg2gene/blob/master/vignettes/Figures/Meta-Analysis_Simplified.png")
knitr::include_graphics("/data/akalin/Projects/AAkalin_reg2gene/reg2gene/vignettes/Figures/Meta-Analysis_Simplified.png")
## ---- echo=FALSE---------------------------------------------------------
# creating datasets
require(GenomicRanges)
require(InteractionSet)
gr2 <- gr <- GRanges(seqnames=rep("chr1",3),IRanges(1:3,3:5))
x <- 1:5
y <- 2:6
z <- 10:14
a <- rep(0,length(x))
GeneInfo <- as.data.frame(matrix(c(rep("gene",3),rep("regulatory",6)),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr) <- DataFrame(cbind(GeneInfo,rbind(x,y,z)))
mcols(gr2) <- DataFrame(cbind(GeneInfo,rbind(x,y,a)))
# create associateReg2Gene output objects, GInteractions will all
# output results
AssocObject <- reg2gene::associateReg2Gene(gr)
AssocObject2 <- reg2gene::associateReg2Gene(gr2)
# input for meta-analysis is list of such objects
associations <- list(AssocObject,AssocObject2)
names(associations) <- c("Roadmap","Blueprint")
# Run metaA
metaAssociations(associations)
## ----fig5, fig.height = 5, fig.width = 3, fig.align = "center"-----------
#knitr::include_graphics("https://github.com/BIMSBbioinfo/reg2gene/blob/master/vignettes/Figures/BenchSimpleE.png")
knitr::include_graphics("/data/akalin/Projects/AAkalin_reg2gene/reg2gene/vignettes/Figures/BenchSimpleE.png")
## ------------------------------------------------------------------------
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[2]
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)
# removing confusing meta-data
mcols(reg2Gene) <- NULL
benchmarkAssociations(reg2Gene,
benchData,
binary=TRUE)
## ---- echo=FALSE---------------------------------------------------------
tmp <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[2]
mcols(tmp) <- NULL
tmp
## ---- echo=FALSE---------------------------------------------------------
tmp <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[5]
mcols(tmp) <- NULL
tmp
## ---- echo=FALSE---------------------------------------------------------
reg2GeneBench <- GInteractions(GRReg1_toy,GRReg1_toy$reg)
Bench <- reg2GeneBench$anchor1.Bench1Exp
Filter <- reg2GeneBench$anchor1.Filter1Exp
mcols(reg2GeneBench) <- NULL
reg2GeneBench$Pval <- seq(0, 1, length.out = length(GRReg1_toy))
reg2GeneBench$Bench <- Bench
reg2GeneBench$Filter <- Filter
reg2GeneBench
## ---- echo=FALSE---------------------------------------------------------
reg2GeneBench <- GInteractions(GRReg1_toy,GRReg1_toy$reg)
Bench <- reg2GeneBench$anchor1.Bench1Exp
Filter <- reg2GeneBench$anchor1.Filter1Exp
mcols(reg2GeneBench) <- NULL
reg2GeneBench$Pval <- seq(0, 1, length.out = length(GRReg1_toy))
reg2GeneBench$Bench <- Bench
reg2GeneBench$Filter <- Filter
confusionMatrix(reg2GeneBench,
thresholdID = "Pval",
thresholdValue = 0.05,
benchCol = "Bench",
prefilterCol = "Filter",
statistics = "ConfusionMatrix")
## ------------------------------------------------------------------------
genomicRegions <- GRanges(c("chr1:1-2", # 1. overlap prom
"chr2:1-2", # 2. overlap enh
"chr3:1-2", # 3. overlap tss +/- 1,000,000
"chr4:1-2")) # 4. do not overlap tss +/- 1,000,000
# CREATE GInteractions test object
annotationsEnh <- GRanges(c("chr1:1-2",
"chr2:1-2",
"chr3:100000-100002",
"chr4:10000001-10000002"))
annotationsGenes <- GRanges(c("chr1:1-2",
"chr2:100000-100002",
"chr3:99999-100002",
"chr4:10000001-10000002"))
annotationsGenes$name=c("gen1","gen2","gen3","gen4")
seqlengths(annotationsEnh) <- seqlengths(annotationsGenes) <- rep(10000002,4)
annotations = GInteractions(annotationsEnh,annotationsGenes)
annotateGenomicRegions(genomicRegions,
annotations =annotations,
identified=T)
## ------------------------------------------------------------------------
annotateGenomicRegions(genomicRegions,
annotations,
identified=F)
## ---- echo=FALSE---------------------------------------------------------
reg2gene::GRReg1_toy
## ------------------------------------------------------------------------
reg2gene::GRReg2_toy
## ------------------------------------------------------------------------
reg2gene::GRReg1_toy[7]
## ------------------------------------------------------------------------
GRReg2_toy[10:12]
## ------------------------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regActivityInputExample <- c(test.bw,test2.bw)
regActivityInputExample
## ------------------------------------------------------------------------
GRReg1_toyGI <- GRReg1_toy
GRReg1_toyGI <- GInteractions(GRReg1_toyGI,GRReg1_toyGI$reg)
GRReg1_toyGI[1:3]
## ----echo=FALSE----------------------------------------------------------
require(InteractionSet)
GRReg1_toyGI <- reg2gene::GRReg1_toy[1]
GRReg1_toyGI <- GInteractions(GRReg1_toyGI,GRReg1_toyGI$reg)
mcols(GRReg1_toyGI) <- mcols(GRReg1_toyGI)[1:3]
GRReg1_toyGI
## ----echo=FALSE----------------------------------------------------------
toy <- reg2gene::GRReg1_toy[1]
mcols(toy) <- mcols(toy)[1:3]
toy
## ------------------------------------------------------------------------
require(reg2gene)
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regActivityInputExample <- c(test.bw,test2.bw)
regActivityInputExample
## ------------------------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",2),"chr2",rep("chr1",3)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3,5,5,5)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3,5,5,5))
bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw),
sampleIDs=c("CellType1","CellType2"))
## ------------------------------------------------------------------------
sampleIDs <- c("/Reverse1.bw","/Forward1.bw","/Reverse2.bw","/Forward2.bw",
"Unstranded1")
sampleIDs
## ------------------------------------------------------------------------
libStrand <- c("+","-","+","-","*")
libStrand
## ----echo=FALSE----------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",2),"chr2",rep("chr1",3)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3,5,5,5)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3,5,5,5))
print("bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw),
normalize=\"quantile\")")
bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw),
normalize="quantile")
## ----echo=FALSE----------------------------------------------------------
test.bw <- system.file("extdata", "test.bw",package = "reg2gene")
test2.bw <- system.file("extdata", "test2.bw",package = "reg2gene")
regTSS_toy <- GRanges(c(rep("chr1",2),"chr2",rep("chr1",3)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3,5,5,5)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3,5,5,5))
print("bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw),
normalize=\"ratio\")")
bwToGeneExp(exons = regTSS_toy,geneActSignals = c(test.bw,test2.bw),
normalize="ratio")
## ----echo=FALSE----------------------------------------------------------
regTSS_toy <- GRanges(c(rep("chr1",4),rep("chr2",2)),
IRanges(c(1,7,9,15,1,15),c(4,8,14,20,4,20)),
c(rep("+",3),rep("-",3)))
regTSS_toy$reg <- regTSS_toy[c(1,1,3:6)]
regTSS_toy$name2 <- regTSS_toy$name <- paste0("TEST_Reg",
c(1,1,3:length(regTSS_toy)))
regTSS_toy
## ---- echo=FALSE---------------------------------------------------------
regTSS_toy <- GRReg1_toy
regTSS_toy$bw1 <- rep(1,length(GRReg1_toy))
regTSS_toy$bw2 <- rep(2,length(GRReg1_toy))
regTSS_toy$bw3 <- rep(3,length(GRReg1_toy))
regReg_toy <- GRReg2_toy
regReg_toy$bw1 <- rep(3,length(regReg_toy))
regReg_toy$bw2 <- rep(4,length(regReg_toy))
print("Result of upstream=5,downstream=5")
regActivityAroundTSS(regReg_toy,regTSS_toy,upstream=5,downstream=5)[1]
## ---- echo=FALSE---------------------------------------------------------
###############################
#STEP 1. Getting random and predefined .8 correlation
require(GenomicRanges)
require(doMC)
require(glmnet)
require(foreach)
require(stringr)
require(qvalue)
####################################
# create example
x <- c(2.000346,2.166255,0.7372374,0.9380581,2.423209,
2.599857,4.216959,2.589133,1.848172,3.039659)
y <- c(2.866875,2.817145,2.1434456,2.9039771,3.819091,5.009990,
5.048476,2.884551,2.780067,4.053136)
corrM <- rbind(x,y)
# define Granges object
gr0 <- GRanges(seqnames=rep("chr1",2),IRanges(1:2,3:4))
GeneInfo <- as.data.frame(matrix(rep(c("gene","regulatory"),each=3),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr0) <- DataFrame(cbind(GeneInfo,corrM))
print("associateReg2Gene(gr0,cores = 1,B=100)")
associateReg2Gene(gr0,cores = 1,B=100,asGInteractions=FALSE)
## ---- echo=FALSE---------------------------------------------------------
require(GenomicRanges)
require(InteractionSet)
gr2 <- gr <- GRanges(seqnames=rep("chr1",3),IRanges(1:3,3:5))
x <- 1:5
y <- 2:6
z <- 10:14
a <- rep(0,length(x))
GeneInfo <- as.data.frame(matrix(c(rep("gene",3),rep("regulatory",6)),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr) <- DataFrame(cbind(GeneInfo,rbind(x,y,z)))
mcols(gr2) <- DataFrame(cbind(GeneInfo,rbind(x,y,a)))
# create associateReg2Gene output objects, GInteractions will all
# output results
AssocObject <- reg2gene::associateReg2Gene(gr)
AssocObject2 <- reg2gene::associateReg2Gene(gr2)
# input for meta-analysis is list of such objects
associations <- list(AssocObject,AssocObject2)
names(associations) <- c("H3K4me1","H327ac")
associations
## ---- echo=FALSE---------------------------------------------------------
require(GenomicRanges)
require(InteractionSet)
gr2 <- gr <- GRanges(seqnames=rep("chr1",3),IRanges(1:3,3:5))
x <- 1:5
y <- 2:6
z <- 10:14
a <- rep(0,length(x))
GeneInfo <- as.data.frame(matrix(c(rep("gene",3),rep("regulatory",6)),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr) <- DataFrame(cbind(GeneInfo,rbind(x,y,z)))
mcols(gr2) <- DataFrame(cbind(GeneInfo,rbind(x,y,a)))
# create associateReg2Gene output objects, GInteractions will all
# output results
AssocObject <- reg2gene::associateReg2Gene(gr)
AssocObject2 <- reg2gene::associateReg2Gene(gr2)
# input for meta-analysis is list of such objects
associations <- list(AssocObject,AssocObject2)
names(associations) <- c("H3K4me1","H327ac")
# Run voteAssociations
voteAssociations(associations,
cutoff.stat="pval",
cutoff.val=0.05,
vote.threshold=0.51)
## ---- echo=FALSE---------------------------------------------------------
# creating datasets
require(GenomicRanges)
require(InteractionSet)
gr2 <- gr <- GRanges(seqnames=rep("chr1",3),IRanges(1:3,3:5))
x <- 1:5
y <- 2:6
z <- 10:14
a <- rep(0,length(x))
GeneInfo <- as.data.frame(matrix(c(rep("gene",3),rep("regulatory",6)),
ncol = 3,byrow = TRUE),stringsAsFactors=FALSE)
colnames(GeneInfo) <- c("featureType","name","name2")
mcols(gr) <- DataFrame(cbind(GeneInfo,rbind(x,y,z)))
mcols(gr2) <- DataFrame(cbind(GeneInfo,rbind(x,y,a)))
# create associateReg2Gene output objects, GInteractions will all
# output results
AssocObject <- reg2gene::associateReg2Gene(gr)
AssocObject2 <- reg2gene::associateReg2Gene(gr2)
# input for meta-analysis is list of such objects
associations <- list(AssocObject,AssocObject2)
names(associations) <- c("Roadmap","Blueprint")
# Run metaA
associations
## ---- echo=FALSE---------------------------------------------------------
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[6]
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)[6]
# removing confusing meta-data
mcols(reg2Gene) <- NULL
mcols(benchData) <- NULL
print("test set:")
reg2Gene
print("benchmark set:")
benchData
print("after benchmarking results in:")
benchmarkAssociations(reg2Gene,
benchData,
binary=TRUE)
## ---- echo=FALSE---------------------------------------------------------
tmp <- GInteractions(GRReg2_toy,GRReg2_toy$reg)[10:12]
mcols(tmp) <- NULL
tmp
## ---- echo=FALSE---------------------------------------------------------
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[7]
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)[10:12]
mcols(reg2Gene) <- NULL
bench <- benchmarkAssociations(reg2Gene,
benchData,
binary=FALSE)
bench
## ---- echo=FALSE---------------------------------------------------------
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)[7]
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)[10:12]
# removing confusing meta-data
mcols(reg2Gene) <- NULL
mcols(benchData) <- NULL
reg2Gene
benchData
benchmarkAssociations(reg2Gene,
benchData,
binary=TRUE)
## ------------------------------------------------------------------------
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)
benchDataList <- list(benchData,reg2Gene)
names(benchDataList) <- c("benchData1","benchData2")
benchmarkAssociations(reg2Gene,
benchDataList,
ignore.strand=TRUE,
binary=FALSE,
nCores = 1)
## ---- echo=FALSE---------------------------------------------------------
reg2GeneBench <- GInteractions(GRReg1_toy,GRReg1_toy$reg)
Bench <- reg2GeneBench$anchor1.Bench1Exp
Filter <- reg2GeneBench$anchor1.Filter1Exp
mcols(reg2GeneBench) <- NULL
reg2GeneBench$Pval <- seq(0, 1, length.out = length(GRReg1_toy))
reg2GeneBench$Bench <- Bench
reg2GeneBench$Filter <- Filter
confusionMatrix(reg2GeneBench,
thresholdID = "Pval",
thresholdValue = 0.05,
benchCol = "Bench",
prefilterCol = "Filter",
statistics = "PPV")
## ---- echo=FALSE---------------------------------------------------------
sessionInfo()
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