Nothing
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
library(COCOA)
data("esr1_chr1")
data("gata3_chr1")
data("nrf1_chr1")
data("atf3_chr1")
data("brcaMCoord1")
data("brcaMethylData1")
data("brcaMetadata")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
myPhen <- "ER_status"
targetVarDF <- brcaMetadata[colnames(brcaMethylData1), myPhen, drop=FALSE]
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
targetVarDF$ER_status <- scale(as.numeric(targetVarDF$ER_status),
center=TRUE, scale=FALSE)
methylCor <- cor(t(brcaMethylData1), targetVarDF$ER_status,
use = "pairwise.complete.obs")
# if the standard deviation of the methylation level
# for a CpG across samples is 0,
# cor() will return NA, so manually set the correlation to 0 for these CpGs
methylCor[is.na(methylCor)] <- 0
colnames(methylCor) <- myPhen
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
GRList <- GRangesList(esr1_chr1, gata3_chr1, atf3_chr1, nrf1_chr1)
regionSetNames <- c("ESR1", "GATA3", "ATF3", "NRF1")
rsScores <- aggregateSignalGRList(signal=methylCor,
signalCoord=brcaMCoord1,
GRList=GRList,
signalCol=myPhen,
scoringMetric="default",
absVal=TRUE)
rsScores$regionSetName <- regionSetNames
rsScores
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
set.seed(100)
nPerm <- 5
permRSScores <- list()
for (i in 1:nPerm) {
# shuffling sample labels
sampleOrder <- sample(1:nrow(targetVarDF), nrow(targetVarDF))
permRSScores[[i]] <- runCOCOA(sampleOrder=sampleOrder,
genomicSignal=brcaMethylData1,
signalCoord=brcaMCoord1,
GRList=GRList,
signalCol=myPhen,
targetVar=targetVarDF,
variationMetric="cor")
permRSScores[[i]]$regionSetName <- regionSetNames
}
permRSScores[1:3]
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
nullDistList <- convertToFromNullDist(permRSScores)
names(nullDistList) <- regionSetNames
nullDistList
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
# p-values based on fitted gamma distributions
gPValDF <- getGammaPVal(rsScores = rsScores,
nullDistList = nullDistList,
signalCol = myPhen,
method = "mme", realScoreInDist = TRUE)
gPValDF <- cbind(gPValDF,
rsScores[, colnames(rsScores)[!(colnames(rsScores)
%in% myPhen)]])
gPValDF <- cbind(gPValDF, regionSetNames)
gPValDF
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
getPermStat(rsScores = rsScores,
nullDistList = nullDistList,
signalCol = myPhen)
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
library(COCOA)
library(data.table)
library(ggplot2)
data("esr1_chr1")
data("gata3_chr1")
data("nrf1_chr1")
data("atf3_chr1")
data("brcaMCoord1")
data("brcaMethylData1")
data("brcaMetadata")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
pca <- prcomp(t(brcaMethylData1))
pcScores <- pca$x
plot(pcScores[, c("PC1", "PC2")])
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
PCsToAnnotate <- paste0("PC", 1:4)
targetVar <- pcScores[, PCsToAnnotate]
targetVar <- as.matrix(scale(targetVar,
center=TRUE, scale=FALSE))
methylCor <- cor(t(brcaMethylData1), targetVar,
use = "pairwise.complete.obs")
# if the standard deviation of the methylation level
# for a CpG across samples is 0,
# cor() will return NA, so manually set the correlation to 0 for these CpGs
methylCor[is.na(methylCor)] <- 0
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
# prepare data
GRList <- GRangesList(esr1_chr1, gata3_chr1, nrf1_chr1, atf3_chr1)
regionSetNames <- c("esr1_chr1", "gata3_chr1", "nrf1_chr1", "atf3_chr1")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
regionSetScores <- aggregateSignalGRList(signal=methylCor,
signalCoord=brcaMCoord1,
GRList=GRList,
signalCol=PCsToAnnotate,
scoringMetric="default")
regionSetScores$regionSetName <- regionSetNames
regionSetScores
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
annoPCScores <- data.frame(pcScores, ER_status=as.factor(brcaMetadata[row.names(pcScores), "ER_status"]))
ggplot(data = annoPCScores, mapping = aes(x=PC1, y=PC2, col=ER_status)) + geom_point() + ggtitle("PCA of a subset of DNA methylation data from breast cancer patients") + theme_classic()
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
plotAnnoScoreDist(rsScores = regionSetScores,
colToPlot = "PC1",
pattern = "GATA3|ESR1",
patternName = "ER-related",
rsNameCol = "regionSetName",
alpha=1)
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
rsScoreHeatmap(regionSetScores,
signalCol=paste0("PC", 1:4),
rsNameCol = "regionSetName",
orderByCol = "PC1",
column_title = "Region sets ordered by score for PC1")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
rsScoreHeatmap(regionSetScores,
signalCol=paste0("PC", 1:4),
rsNameCol = "regionSetName",
orderByCol = "PC2",
column_title = "Region sets ordered by score for PC2")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
set.seed(100)
nPerm <- 5
permRSScores <- list()
for (i in 1:nPerm) {
# shuffling sample labels
sampleOrder <- sample(1:nrow(targetVar), nrow(targetVar))
permRSScores[[i]] <- runCOCOA(sampleOrder=sampleOrder,
genomicSignal=brcaMethylData1,
signalCoord=brcaMCoord1,
GRList=GRList,
signalCol=PCsToAnnotate,
targetVar=targetVar,
variationMetric="cor")
permRSScores[[i]]$regionSetName <- regionSetNames
}
permRSScores[1:3]
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
nullDistList <- convertToFromNullDist(permRSScores)
names(nullDistList) <- regionSetNames
nullDistList
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
# p-values based on fitted gamma distributions
gPValDF <- getGammaPVal(rsScores = regionSetScores,
nullDistList = nullDistList,
signalCol = PCsToAnnotate,
method = "mme", realScoreInDist = TRUE)
gPValDF <- cbind(gPValDF,
regionSetScores[, colnames(regionSetScores)[!(colnames(regionSetScores)
%in% PCsToAnnotate)]])
gPValDF <- cbind(gPValDF, regionSetNames)
gPValDF
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
getPermStat(rsScores = regionSetScores,
nullDistList = nullDistList,
signalCol = PCsToAnnotate)
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
wideGRList <- lapply(GRList, resize, width=14000, fix="center")
fcsProfile <- lapply(wideGRList, function(x) getMetaRegionProfile(signal=methylCor,
signalCoord=brcaMCoord1,
regionSet=x,
signalCol=PCsToAnnotate,
binNum=21))
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
# average FCS from each PC to normalize so PCs can be compared with each other
avFCS <- apply(X=methylCor[, PCsToAnnotate],
MARGIN=2,
FUN=function(x) mean(abs(x)))
# normalize
fcsProfile <- lapply(fcsProfile,
FUN=function(x) as.data.frame(mapply(FUN = function(y, z) x[, y] - z,
y=PCsToAnnotate, z=avFCS)))
binID = 1:nrow(fcsProfile[[1]])
fcsProfile <- lapply(fcsProfile, FUN=function(x) cbind(binID, x))
# for the plot scale
maxVal <- max(sapply(fcsProfile, FUN=function(x) max(x[, PCsToAnnotate])))
minVal <- min(sapply(fcsProfile, FUN=function(x) min(x[, PCsToAnnotate])))
# convert to long format for plots
fcsProfile <- lapply(X=fcsProfile, FUN=function(x) tidyr::gather(data=x, key="PC", value="meanFCS", PCsToAnnotate))
fcsProfile <- lapply(fcsProfile,
function(x){x$PC <- factor(x$PC, levels=PCsToAnnotate); return(x)})
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
wrapper <- function(x, ...) paste(strwrap(x, ...), collapse="\n")
profilePList <- list()
for (i in seq_along(fcsProfile)) {
thisRS <- fcsProfile[[i]]
profilePList[[i]] <- ggplot(data=thisRS,
mapping=aes(x=binID , y=meanFCS)) +
geom_line() + ylim(c(minVal, maxVal)) + facet_wrap(facets="PC") +
ggtitle(label=wrapper(regionSetNames[i], width=30)) +
xlab("Genome around region set, 14 kb") +
ylab("Normalized mean FCS") +
theme(panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
profilePList[[i]]
}
profilePList[[1]]
profilePList[[2]]
profilePList[[3]]
profilePList[[4]]
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
signalAlongAxis(genomicSignal=brcaMethylData1,
signalCoord=brcaMCoord1,
regionSet=esr1_chr1,
sampleScores=pcScores,
topXVariables = 100,
variableScores = abs(methylCor[, "PC1"]),
orderByCol="PC1", cluster_columns=TRUE,
column_title = "Individual cytosine/CpG",
name = "DNA methylation level",
show_row_names=FALSE)
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
signalAlongAxis(genomicSignal=brcaMethylData1,
signalCoord=brcaMCoord1,
regionSet=nrf1_chr1,
sampleScores=pcScores,
topXVariables = 100,
variableScores = abs(methylCor[, "PC1"]),
orderByCol="PC1",
cluster_columns=TRUE,
column_title = "Individual cytosine/CpG",
name = "DNA methylation level",
show_row_names=FALSE)
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
regionQuantileByTargetVar(signal = methylCor,
signalCoord = brcaMCoord1,
regionSet = esr1_chr1,
rsName = "Estrogen receptor (chr1)",
signalCol=paste0("PC", 1:4),
maxRegionsToPlot = 8000,
cluster_rows = TRUE,
cluster_columns = FALSE,
column_title = rsName,
name = "Percentile of feature contribution scores in PC")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
regionQuantileByTargetVar(signal = methylCor,
signalCoord = brcaMCoord1,
regionSet = nrf1_chr1,
rsName = "NRF1 (chr1)",
signalCol=paste0("PC", 1:4),
maxRegionsToPlot = 8000,
cluster_rows = TRUE,
cluster_columns = FALSE,
column_title = rsName,
name = "Percentile of feature contribution scores in PC")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
library(COCOA)
data("esr1_chr1")
data("gata3_chr1")
data("nrf1_chr1")
data("atf3_chr1")
data("brcaATACCoord1")
data("brcaATACData1")
data("brcaMetadata")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
myPhen <- "ER_status"
targetVarDF <- brcaMetadata[colnames(brcaATACData1), myPhen, drop=FALSE]
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
targetVarDF$ER_status <- scale(as.numeric(targetVarDF$ER_status),
center=TRUE, scale=FALSE)
atacCor <- cor(t(brcaATACData1), targetVarDF$ER_status,
use = "pairwise.complete.obs")
# if the standard deviation of the epigenetic signal
# for a peak region across samples is 0,
# cor() will return NA, so manually set the correlation to 0 for these regions
atacCor[is.na(atacCor)] <- 0
colnames(atacCor) <- myPhen
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
GRList <- GRangesList(esr1_chr1, gata3_chr1, atf3_chr1, nrf1_chr1)
regionSetNames <- c("ESR1", "GATA3", "ATF3", "NRF1")
rsScores <- aggregateSignalGRList(signal=atacCor,
signalCoord=brcaATACCoord1,
GRList=GRList,
signalCol=myPhen,
scoringMetric="default",
absVal=TRUE)
rsScores$regionSetName <- regionSetNames
rsScores
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
set.seed(100)
nPerm <- 5
permRSScores <- list()
for (i in 1:nPerm) {
# shuffling sample labels
sampleOrder <- sample(1:nrow(targetVarDF), nrow(targetVarDF))
permRSScores[[i]] <- runCOCOA(sampleOrder=sampleOrder,
genomicSignal=brcaATACData1,
signalCoord=brcaATACCoord1,
GRList=GRList,
signalCol=myPhen,
targetVar=targetVarDF,
variationMetric="cor")
permRSScores[[i]]$regionSetName <- regionSetNames
}
permRSScores[1:3]
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
nullDistList <- convertToFromNullDist(permRSScores)
names(nullDistList) <- regionSetNames
nullDistList
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
# p-values based on fitted gamma distributions
gPValDF <- getGammaPVal(rsScores = rsScores,
nullDistList = nullDistList,
signalCol = myPhen,
method = "mme", realScoreInDist = TRUE)
gPValDF <- cbind(gPValDF,
rsScores[, colnames(rsScores)[!(colnames(rsScores)
%in% myPhen)]])
gPValDF <- cbind(gPValDF, regionSetNames)
gPValDF
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
getPermStat(rsScores = rsScores,
nullDistList = nullDistList,
signalCol = myPhen)
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
library(COCOA)
library(data.table)
library(ggplot2)
data("esr1_chr1")
data("gata3_chr1")
data("nrf1_chr1")
data("atf3_chr1")
data("brcaATACCoord1")
data("brcaATACData1")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
pca <- prcomp(t(brcaATACData1))
pcScores <- pca$x
plot(pcScores[, c("PC1", "PC2")])
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
PCsToAnnotate <- paste0("PC", 1:4)
targetVar <- pcScores[, PCsToAnnotate]
targetVar <- as.matrix(scale(targetVar,
center=TRUE, scale=FALSE))
atacCor <- cor(t(brcaATACData1), targetVar,
use = "pairwise.complete.obs")
# if the standard deviation of the ATAC-seq counts
# for a peak region across samples is 0,
# cor() will return NA, so manually set the correlation to 0 for these regions
atacCor[is.na(atacCor)] <- 0
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
# prepare data
GRList <- GRangesList(esr1_chr1, gata3_chr1, nrf1_chr1, atf3_chr1)
regionSetNames <- c("esr1_chr1", "gata3_chr1", "nrf1_chr1", "atf3_chr1")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
regionSetScores <- aggregateSignalGRList(signal=atacCor,
signalCoord=brcaATACCoord1,
GRList=GRList,
signalCol=PCsToAnnotate,
scoringMetric="default")
regionSetScores$regionSetName <- regionSetNames
regionSetScores
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
annoPCScores <- data.frame(pcScores, ER_status=as.factor(brcaMetadata[row.names(pcScores), "ER_status"]))
ggplot(data = annoPCScores, mapping = aes(x=PC1, y=PC2, col=ER_status)) + geom_point() + ggtitle("PCA of a subset of chromatin accessibility data from breast cancer patients") + theme_classic()
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
plotAnnoScoreDist(rsScores = regionSetScores,
colToPlot = "PC1",
pattern = "GATA3|ESR1",
patternName = "ER-related",
rsNameCol = "regionSetName",
alpha=1)
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
rsScoreHeatmap(regionSetScores,
signalCol=paste0("PC", 1:4),
rsNameCol = "regionSetName",
orderByCol = "PC1",
column_title = "Region sets ordered by score for PC1")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
rsScoreHeatmap(regionSetScores,
signalCol=paste0("PC", 1:4),
rsNameCol = "regionSetName",
orderByCol = "PC2",
column_title = "Region sets ordered by score for PC2")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
set.seed(100)
nPerm <- 5
permRSScores <- list()
for (i in 1:nPerm) {
# shuffling sample labels
sampleOrder <- sample(1:nrow(targetVar), nrow(targetVar))
permRSScores[[i]] <- runCOCOA(sampleOrder=sampleOrder,
genomicSignal=brcaATACData1,
signalCoord=brcaATACCoord1,
GRList=GRList,
signalCol=PCsToAnnotate,
targetVar=targetVar,
variationMetric="cor")
permRSScores[[i]]$regionSetName <- regionSetNames
}
permRSScores[1:3]
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
nullDistList <- convertToFromNullDist(permRSScores)
names(nullDistList) <- regionSetNames
nullDistList
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
# p-values based on fitted gamma distributions
gPValDF <- getGammaPVal(rsScores = regionSetScores,
nullDistList = nullDistList,
signalCol = PCsToAnnotate,
method = "mme", realScoreInDist = TRUE)
gPValDF <- cbind(gPValDF,
regionSetScores[, colnames(regionSetScores)[!(colnames(regionSetScores)
%in% PCsToAnnotate)]])
gPValDF <- cbind(gPValDF, regionSetNames)
gPValDF
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
getPermStat(rsScores = regionSetScores,
nullDistList = nullDistList,
signalCol = PCsToAnnotate)
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
wideGRList <- lapply(GRList, resize, width=14000, fix="center")
fcsProfile <- lapply(wideGRList, function(x) getMetaRegionProfile(signal=atacCor,
signalCoord=brcaATACCoord1,
regionSet=x,
signalCol=PCsToAnnotate,
binNum=21))
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
# average FCS from each PC to normalize so PCs can be compared with each other
avFCS <- apply(X=atacCor[, PCsToAnnotate],
MARGIN=2,
FUN=function(x) mean(abs(x)))
# normalize
fcsProfile <- lapply(fcsProfile,
FUN=function(x) as.data.frame(mapply(FUN = function(y, z) x[, y] - z,
y=PCsToAnnotate, z=avFCS)))
binID = 1:nrow(fcsProfile[[1]])
fcsProfile <- lapply(fcsProfile, FUN=function(x) cbind(binID, x))
# for the plot scale
maxVal <- max(sapply(fcsProfile, FUN=function(x) max(x[, PCsToAnnotate])))
minVal <- min(sapply(fcsProfile, FUN=function(x) min(x[, PCsToAnnotate])))
# convert to long format for plots
fcsProfile <- lapply(X=fcsProfile, FUN=function(x) tidyr::gather(data=x, key="PC", value="meanFCS", PCsToAnnotate))
fcsProfile <- lapply(fcsProfile,
function(x){x$PC <- factor(x$PC, levels=PCsToAnnotate); return(x)})
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
wrapper <- function(x, ...) paste(strwrap(x, ...), collapse="\n")
profilePList <- list()
for (i in seq_along(fcsProfile)) {
thisRS <- fcsProfile[[i]]
profilePList[[i]] <- ggplot(data=thisRS,
mapping=aes(x=binID , y=meanFCS)) +
geom_line() + ylim(c(minVal, maxVal)) + facet_wrap(facets="PC") +
ggtitle(label=wrapper(regionSetNames[i], width=30)) +
xlab("Genome around region set, 14 kb") +
ylab("Normalized mean FCS") +
theme(panel.grid.major.x=element_blank(),
panel.grid.minor.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank())
profilePList[[i]]
}
profilePList[[1]]
profilePList[[2]]
profilePList[[3]]
profilePList[[4]]
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
signalAlongAxis(genomicSignal=brcaATACData1,
signalCoord=brcaATACCoord1,
regionSet=esr1_chr1,
sampleScores=pcScores,
orderByCol="PC1", cluster_columns=TRUE,
column_title = "Individual ATAC-seq region",
name = "Normalized signal in ATAC-seq regions",
show_row_names=FALSE,
show_column_names=FALSE)
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
signalAlongAxis(genomicSignal=brcaATACData1,
signalCoord=brcaATACCoord1,
regionSet=nrf1_chr1,
sampleScores=pcScores,
orderByCol="PC1",
cluster_columns=TRUE,
column_title = "Individual ATAC-seq region",
name = "Normalized signal in ATAC-seq regions",
show_row_names=FALSE,
show_column_names=FALSE)
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
regionQuantileByTargetVar(signal = atacCor,
signalCoord = brcaATACCoord1,
regionSet = esr1_chr1,
rsName = "Estrogen receptor (chr1)",
signalCol=paste0("PC", 1:4),
maxRegionsToPlot = 8000,
cluster_rows = TRUE,
cluster_columns = FALSE,
column_title = rsName,
name = "Percentile of feature contribution scores in PC")
## ---- eval=TRUE, message=FALSE, warning=FALSE---------------------------------
regionQuantileByTargetVar(signal = atacCor,
signalCoord = brcaATACCoord1,
regionSet = nrf1_chr1,
rsName = "NRF1 (chr1)",
signalCol=paste0("PC", 1:4),
maxRegionsToPlot = 8000,
cluster_rows = TRUE,
cluster_columns = FALSE,
column_title = rsName,
name = "Percentile of feature contribution scores in PC")
## ---- eval=FALSE, message=FALSE, warning=FALSE--------------------------------
# library(LOLA)
#
# # reading in the region sets
# # load LOLA database
# lolaPath <- paste0("path/to/LOLACore/genomeVersion/")
# regionSetDB <- loadRegionDB(lolaPath)
#
# # metadata about the region sets
# loRegionAnno <- regionSetDB$regionAnno
# lolaCoreRegionAnno <- loRegionAnno
# collections <- c("cistrome_cistrome", "cistrome_epigenome", "codex",
# "encode_segmentation", "encode_tfbs", "ucsc_features")
# collectionInd <- lolaCoreRegionAnno$collection %in% collections
# lolaCoreRegionAnno <- lolaCoreRegionAnno[collectionInd, ]
# regionSetName <- lolaCoreRegionAnno$filename
# regionSetDescription <- lolaCoreRegionAnno$description
#
# # the actual region sets
# GRList <- GRangesList(regionSetDB$regionGRL[collectionInd])
#
# # since we have what we need, we can delete this to free up memory
# rm("regionSetDB")
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