R/rnaCrosslinkDataSetMethodsAndHelpers.R
In JLP-BioInf/comradesOO: Analysis of RNA Crosslinking Data
Defines functions
makeExamplernaCrosslinkDataSet
swapInputs3
swapInputs2
swapInputs
Documented in
makeExamplernaCrosslinkDataSet
swapInputs
swapInputs2
swapInputs3
#' @include rnaCrosslinkDataSet.R
NULL
################################################################################
# Methods functions and methods relating to creation and use of
# rnaCrosslinkDataSets
################################################################################
#' featureInfo
#'
#' Produces a list list of 2 elemnts 'transcript' and 'family'
#' Each element contains a table with the counts for each RNA in each sample that
#' interact with the target RNA
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#'
#'
#' @name featureInfo
#' @docType methods
#' @aliases featureInfo,rnaCrosslinkDataSet-method
#' @rdname featureInfo
#' @return A list - Feature level and transcript level counts for each sample
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' featureInfo(cds)
#'
#' @export
setGeneric("featureInfo",
function(cds)
standardGeneric("featureInfo"))
setMethod("featureInfo",
"rnaCrosslinkDataSet",
function(cds) {
alteredInputList = list()
TE = rnas(cds)
InputList = getData(x = cds,
data = "InputFiles",
type = "original")
for (Input in 1:length(InputList)) {
controlInput = InputList[[Input]]
#get the right columns
controlInput = as.data.frame(cbind(
as.character(controlInput$V4),
as.character(controlInput$V10)
))
#get only those lines with the RNA
controlInput = controlInput
#remove the factor levels
controlInput$V1 = as.character(controlInput$V1)
controlInput$V2 = as.character(controlInput$V2)
#Swap the columns around
controlInputtmp1 = controlInput[controlInput$V1 == TE, ]
controlInputtmp2 = controlInput[!(controlInput$V1 == TE), ]
controlInputtmp2 = rev(controlInputtmp2)
colnames(controlInputtmp2) = c("V1", "V2")
controlInput = as.data.frame(rbind(controlInputtmp1, controlInputtmp2))
#add to list
alteredInputList[[TE]][[Input]] = controlInput
}
##############################
# Now Combine the two
##############################
df = list()
df[[TE]] = data.frame()
aggList = list()
totalNames = c()
for (Input in 1:length(alteredInputList[[TE]])) {
sampleInput = alteredInputList[[TE]][[Input]]
sData = sampleInput[sampleInput$V1 == TE, ]
freqSample2 = aggregate(sData$V1, by = list(sData$V2), FUN = length)
freqSample = freqSample2$x
names(freqSample) = freqSample2$Group.1
aggList[[TE]][[Input]] = freqSample
# Get the total features that exist in the dataset
totalNames = unique(sort(c(totalNames, names(freqSample))))
}
# geta. matrix for plotting
tmpMat = matrix(0, nrow = length(totalNames), ncol = length(InputList))
row.names(tmpMat) = totalNames
colnames(tmpMat) = sampleNames(cds)
for (i in 1:nrow(tmpMat)) {
for (j in 1:ncol(tmpMat)) {
tmpMat[i, j] = aggList[[TE]][[j]][row.names(tmpMat)[i]]
}
}
tmpMat[is.na(tmpMat)] <- 0.00001
df[[TE]] = as.data.frame(tmpMat)
# now make a stats list
statsList = list()
statsList[[TE]] = df[[TE]]
statsList[[TE]]$ID = sapply(row.names(statsList[[TE]]), function(x)
tail(strsplit(x, "_")[[1]],n=1), USE.NAMES = FALSE)
statsList[[TE]]
# Now get the stats from aggregate
aggList = list()
aggList2 = list()
aggList[[TE]] = aggregate(. ~ ID , statsList[[TE]], sum)
#get sample Table
st = sampleTable(cds)
#find out which samples have controls
samples = st[which(duplicated(st$sample)), "sample"]
for (i in samples) {
# now get the control and sample index
control = as.numeric(which(st$sample == i &
st$group == "c")) + 1
sample = as.numeric(which(st$sample == i &
st$group == "s")) + 1
factor1 = sum(aggList[[TE]][, sample]) / sum(aggList[[TE]][, control])
aggList[[TE]][, paste("norm", i, sep = "_")] = aggList[[TE]][, sample] / (aggList[[TE]][, control] * factor1)
}
data = melt(aggList[[TE]], id.vars = c("ID"))
samples = paste("norm", samples, sep = "_")
plot(ggplot() +
geom_boxplot(data = data[data$variable %in% samples, ], aes(
x = reorder(data[data$variable %in% samples, ]$ID,
data[data$variable %in% samples, ]$value,
FUN = median), y = log2(value)
)) +
geom_point(data = data[data$variable %in% samples, ], aes(
x = reorder(data[data$variable %in% samples, ]$ID,
data[data$variable %in% samples, ]$value,
FUN = median),
y = log2(value),
fill = ID
)) +
geom_hline(yintercept = 0, colour = "darkred") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) )
featureStats = list()
featureStats[["transcript"]] = df[[TE]]
featureStats[["family"]] = aggList[[TE]]
return(featureStats)
})
#' topInteractions
#'
#' This method prints the top transcript interactions that have the most duplexes
#' assigned
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#' @param ntop the number of entries to display
#'
#' @name topInteractions
#' @docType methods
#' @rdname topInteractions
#' @aliases topInteractions,rnaCrosslinkDataSet-method
#' @return A table, the number of counts per sample per interaction
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' topInteractions(cds)
#'
#' @export
setGeneric("topInteractions",
function(cds,
ntop = 10)
standardGeneric("topInteractions"))
setMethod("topInteractions",
"rnaCrosslinkDataSet",
function(cds,
ntop = 10) {
ci = group(cds)[["c"]]
si = group(cds)[["s"]]
df= data.frame()
for (i in names(InputFiles(cds)[["all"]][["all"]])) {
same = which(InputFiles(cds)[["all"]][["all"]][[i]]$V4 ==
InputFiles(cds)[["all"]][["all"]][[i]]$V10 )
notSame = which(InputFiles(cds)[["all"]][["all"]][[i]]$V4 !=
InputFiles(cds)[["all"]][["all"]][[i]]$V10)
s = paste(InputFiles(cds)[["all"]][["all"]][[i]]$V4[same],
InputFiles(cds)[["all"]][["all"]][[i]]$V10[same], sep = "::")
#s = unlist(lapply(s, function(x)
# unlist(strsplit(x,split = "::"))[1] ))
s = as.data.frame(table(s))
s$type="intra"
s$sample = i
colnames(s) = c("RNA","reads","type","sample")
t = paste(InputFiles(cds)[["all"]][["all"]][[i]]$V4[notSame],
InputFiles(cds)[["all"]][["all"]][[i]]$V10[notSame], sep = "::")
#t1 = unlist(lapply(t, function(x)
# unlist(strsplit(x,split = "::"))[1] ))
#t2 = unlist(lapply(t, function(x)
# unlist(strsplit(x,split = "::"))[2] ))
#t = c(t1,t2)
t = as.data.frame(table(t))
t$type="inter"
t$sample = i
colnames(t) = c("RNA","reads","type","sample")
df =rbind.data.frame(df,t,s)
}
df = dcast(df,formula = RNA + type ~ sample, value.var = "reads")
ci = sampleTable(cds)[ci,"sampleName"]
si = sampleTable(cds)[si,"sampleName"]
if(length(si) == 1){
df$samples = df[,si]
df$control = df[,ci]
df$enrichment = df$samples / df$control
}else{
df$samples = rowSums(df[,si],na.rm = TRUE)
df$control = rowSums(df[,ci],na.rm = TRUE)
df$enrichment = df$samples / df$control
}
df = df[order(df$samples,decreasing = TRUE),]
df = df[1:ntop,]
df
})
#' topTranscripts
#'
#' This method prints the top transcripts that have the most duplexes
#' assigned
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#' @param ntop the number of entries to display
#'
#' @name topTranscripts
#' @docType methods
#' @rdname topTranscripts
#' @aliases topTranscripts,rnaCrosslinkDataSet-method
#' @return A table, the number of counts per sample per transcript
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' topTranscripts(cds)
#' @export
#'
setGeneric("topTranscripts",
function(cds,
ntop = 10)
standardGeneric("topTranscripts"))
setMethod("topTranscripts",
"rnaCrosslinkDataSet",
function(cds,
ntop = 10) {
ci = group(cds)[["c"]]
si = group(cds)[["s"]]
df= data.frame()
for (i in names(InputFiles(cds)[["all"]][["all"]])) {
same = which(InputFiles(cds)[["all"]][["all"]][[i]]$V4 ==
InputFiles(cds)[["all"]][["all"]][[i]]$V10 )
notSame = which(InputFiles(cds)[["all"]][["all"]][[i]]$V4 !=
InputFiles(cds)[["all"]][["all"]][[i]]$V10)
s = paste(InputFiles(cds)[["all"]][["all"]][[i]]$V4[same],
InputFiles(cds)[["all"]][["all"]][[i]]$V10[same], sep = "::")
s = unlist(lapply(s, function(x)
unlist(strsplit(x,split = "::"))[1] ))
s = as.data.frame(table(s))
s$type="intra"
s$sample = i
colnames(s) = c("RNA","reads","type","sample")
t = paste(InputFiles(cds)[["all"]][["all"]][[i]]$V4[notSame],
InputFiles(cds)[["all"]][["all"]][[i]]$V10[notSame], sep = "::")
t1 = unlist(lapply(t, function(x)
unlist(strsplit(x,split = "::"))[1] ))
t2 = unlist(lapply(t, function(x)
unlist(strsplit(x,split = "::"))[2] ))
t = c(t1,t2)
t = as.data.frame(table(t))
t$type="inter"
t$sample = i
colnames(t) = c("RNA","reads","type","sample")
df =rbind.data.frame(df,t,s)
}
df = dcast(df,formula = RNA + type ~ sample, value.var = "reads")
ci = sampleTable(cds)[ci,"sampleName"]
si = sampleTable(cds)[si,"sampleName"]
if(length(si) == 1){
df$samples = df[,si]
df$control = df[,ci]
df$enrichment = df$samples / df$control
}else{
df$samples = rowSums(df[,si],na.rm = TRUE)
df$control = rowSums(df[,ci],na.rm = TRUE)
df$enrichment = df$samples / df$control
}
df = df[order(df$samples,decreasing = TRUE),]
df = df[1:ntop,]
df
})
#' topInteracters
#'
#' This method prints the top transcripts that have the most duplexes
#' assigned that interact with the transcript of interest
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#' @param ntop the number of entries to display
#' @param sds known bug, doesn't work for small data sets fix incoming
#'
#' @name topInteracters
#' @docType methods
#' @rdname topInteracters
#' @aliases topInteracters,rnaCrosslinkDataSet-method
#' @return A table, the number of counts per sample per interacting transcript
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' topInteracters(cds, sds = TRUE)
#'
#' @export
setGeneric("topInteracters",
function(cds,
ntop = 10,
sds = TRUE)
standardGeneric("topInteracters"))
setMethod("topInteracters",
"rnaCrosslinkDataSet",
function(cds,
ntop = 10,
sds = TRUE) {
if(sds != TRUE){
ci = group(cds)[["c"]]
si = group(cds)[["s"]]
df= data.frame()
for (i in names(InputFiles(cds)[[rnas(cds)]][["noHost"]])) {
iFile = InputFiles(cds)[[rnas(cds)]][["noHost"]][[i]]
same = which(iFile$V4 == iFile$V10 )
notSame = which(iFile$V4 != iFile$V10 )
s = iFile$V4[same]
s = as.data.frame(table(s))
s$type="intra"
s$sample = i
colnames(s) = c("RNA","reads","type","sample")
v4rna = which(iFile$V4[notSame] == rnas(cds))
t1 = iFile$V4[notSame][-v4rna]
t2 = iFile$V10[notSame][v4rna]
t = c(t1,t2)
if(length(notSame) == 0){
df =rbind.data.frame(df,t,c(NA,NA, "inter", i),stringsAsFactors = F)
}else{
t = as.data.frame(table(t),stringsAsFactors = F)
t$type="inter"
t$sample = i
colnames(t) = c("RNA","reads","type","sample")
}
if(length(same) == 0){ df =rbind.data.frame(df,s,c(NA,NA, "inter", i),stringsAsFactors = F)
}else{
df =rbind.data.frame(df,t,s,stringsAsFactors = F)
}
}
df$reads = as.numeric(df$reads)
df = dcast(df,formula = RNA + type ~ sample, value.var = "reads")
ci = sampleTable(cds)[ci,"sampleName"]
si = sampleTable(cds)[si,"sampleName"]
if(length(si) == 1){
df$samples = df[,si]
df$control = df[,ci]
df$enrichment = df$samples / df$control
}else{
df$samples = rowSums(df[,si],na.rm = TRUE)
df$control = rowSums(df[,ci],na.rm = TRUE)
df$enrichment = df$samples / df$control
}
df = df[order(df$samples,decreasing = TRUE),]
df = df[1:ntop,]
df
}
})
#' getInteractions
#'
#' This method returns a table of interactions of an RNA (interactor) on the RNA of interest.
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#' @param interactors A vector containing the names of RNAs to show interactions with
#'
#'
#' @name getInteractions
#' @docType methods
#' @rdname getInteractions
#' @return A table showing the read coverage of the specified interacting RNAs
#' @aliases getInteractions,rnaCrosslinkDataSet-method
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' getInteractions(cds, c("transcript1","transcript2"))
#'
#' @export
setGeneric("getInteractions",
function(cds,
interactors)
standardGeneric("getInteractions"))
setMethod("getInteractions",
"rnaCrosslinkDataSet",
function(cds,
interactors) {
seq2 <- Vectorize(seq.default, vectorize.args = c("from", "to"))
# subset the Input files based on the interactor of choice
table = data.frame()
for (interactor in interactors){
for (i in names(InputFiles(cds)[[rnas(cds)]][["host"]])) {
x = InputFiles(cds)[[rnas(cds)]][["host"]][[i]]
x = x[x$V10 == interactor, ]
if (nrow(x) == 0) {
v = as.data.frame(0)
}
else {
starts = x$V7
ends = x$V8
v = as.data.frame(unlist(seq2(from = starts, to = ends)))
if(ncol(v) > 1){
next
}
}
v$rna = interactor
v$sample = i
colnames(v) = c("Position", "rna", "sample")
table = rbind.data.frame(table, v)
}
}
colnames(table) = c("Position", "rna", "sample")
table = aggregate(
table$Position,
by = list(table$rna,
table$sample,
table$Position),
FUN = length
)
colnames(table) = c("rna", "sample", "Position", "depth")
table
})
#' getReverseInteractions
#'
#' This method prints interactions of the RNA of interest on another RNA
#' transcript.
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#' @param interactor The rna to show interactions with
#'
#' @name getReverseInteractions
#' @docType methods
#' @rdname getReverseInteractions
#' @aliases getReverseInteractions,rnaCrosslinkDataSet-method
#' @return A long format table shoing the read coverage of chosen RNA
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' getReverseInteractions(cds, 'transcript2')
#'
#' @export
setGeneric("getReverseInteractions",
function(cds,
interactor)
standardGeneric("getReverseInteractions"))
setMethod("getReverseInteractions",
"rnaCrosslinkDataSet",
function(cds,
interactor) {
seq2 <- Vectorize(seq.default, vectorize.args = c("from", "to"))
# subset the Input files based on the interacter of choice
table = data.frame()
for (i in names(InputFiles(cds)[[rnas(cds)]][["host"]])) {
x = InputFiles(cds)[[rnas(cds)]][["host"]][[i]]
x = x[x$V10 == interactor, ]
if (nrow(x) == 0) {
v = as.data.frame(0)
v$rna = interactor
v$sample = i
colnames(v) = c("Position", "rna", "sample")
table = rbind.data.frame(table, v)
} else {
starts = x$V13
ends = x$V14
v = as.data.frame(unlist(seq2(from = starts, to = ends)))
v$rna = interactor
v$sample = i
colnames(v) = c("Position", "rna", "sample")
table = rbind.data.frame(table, v)
}
}
colnames(table) = c("Position", "rna", "sample")
table = aggregate(
table$Position,
by = list(table$rna,
table$sample,
table$Position),
FUN = length
)
colnames(table) = c("rna", "sample", "Position", "depth")
table
})
#' swapInputs
#'
#' Swap the table to ensure that 3 prime most duplex side is on he left of the
#' table used to make one sides heatmaps and other reasons where having the
#' left of the table coming after the right side is a problem. Different from
#' swapInputs as it ensure that BOTH duplex sides originate from the RNA of
#' interest.
#'
#' @param InputList the original InputList created with readInputFiles or subsetInputList
#' @param rna The rna of interest
#'
#' @name swapInputs
#' @docType methods
#' @rdname swapInputs
#' @return A list of "swapped" Input datas
#'
swapInputs = function(InputList,
rna) {
InputListSwap = InputList
for (Input in 1:length(InputList)) {
InputListS = InputList[[Input]][InputList[[Input]]$V4 == rna |
InputList[[Input]]$V10 == rna, ]
comb = InputListS
InputListSwap[[Input]] = comb
}
return(InputListSwap)
}
#' swapInputs2
#'
#' Swap the table to ensure that 3 prime most duplex side is on the left of the table
#' used to make one sides heatmaps and other reasons where having the left of the table
#' coming after the right side is a problem. Different from swapInputs as it
#' ensure that BOTH duplex sides originate from the RNA of interest.
#' @param InputList the original InputList created with readInputFiles or subsetInputList
#' @param rna The rna of interest
#' @name swapInputs2
#' @docType methods
#' @rdname swapInputs2
#' @return A list of "swapped" Input data
#'
swapInputs2 = function(InputList,
rna) {
for (Input in 1:length(InputList)) {
InputList18S = InputList[[Input]][as.character(InputList[[Input]]$V4) == rna &
as.character(InputList[[Input]]$V10) == rna, ]
tmp1 = InputList18S[InputList18S$V7 < InputList18S$V13, ]
tmp2 = InputList18S[InputList18S$V7 > InputList18S$V13, ]
tmp2 = tmp2[, c(
"V1",
"V2",
"V3",
"V4",
"V11",
"V12",
"V13",
"V14",
"V15",
"V10",
"V5",
"V6",
"V7",
"V8",
"V9"
)]
colnames(tmp2) = colnames(tmp1)
comb = rbind.data.frame(tmp1, tmp2)
InputList[[Input]] = comb
}
return(InputList)
}
#' swapInputs3
#'
#' Swap the table to ensure that 3 prime most duplex side is ont he left of the table
#' used to make one sides heatmaps and other reasons where having the left of the table
#' coming after the right side is a problem. Different from swapInputs as it
#' ensure that BOTH duplex sides originate from the RNA of interest.
#' @param InputList the original InputList created with readInputFiles or subsetInputList
#' @param rna The rna of interest
#'
#' @return A list of "swapped" Input datas
#' @name swapInputs3
#' @docType methods
#' @rdname swapInputs3
swapInputs3 = function(InputList, rna) {
InputListSwap = InputList
for (Input in 1:length(InputList)) {
InputListS = InputList[[Input]][InputList[[Input]]$V4 == rna |
InputList[[Input]]$V10 == rna, ]
tmp1 = InputListS[InputListS$V4 == rna, ]
tmp2 = InputListS[InputListS$V4 != rna, ]
tmp2 = tmp2[, c(
"V1",
"V2",
"V3",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9"
)]
colnames(tmp2) = colnames(tmp1)
comb = rbind.data.frame(tmp1, tmp2, stringsAsFactors = F)
InputListSwap[[Input]] = comb
}
return(InputListSwap)
}
#' makeExamplernaCrosslinkDataSet
#'
#' Creat a minimal example rnaCrosslinkdataSetObject
#'
#' @return An example rnaCrosslinkDataSet objext
#' @name makeExamplernaCrosslinkDataSet
#' @rdname makeExamplernaCrosslinkDataSet
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' @export
makeExamplernaCrosslinkDataSet = function() {
c4 = c(rep("transcript1",100),rep("transcript2",100) )
c10 = c(rep("transcript1",200) )
c1 = 1:200
c2 = rep(paste(rep("A", 40), collapse = ""),200)
c3 = rep(".",200)
c9 = rep(".",200)
c15 = rep(".",200)
c5 = rep(1,200)
c11 = rep(21,200)
c6 = rep(20,200)
c12= rep(40,200)
# short distance 50
c7 = sample(1:5, 50, replace = TRUE)
c8 = sample(20:25, 50, replace = TRUE)
c13 = sample(20:25, 50, replace = TRUE)
c14 = sample(40:45, 50, replace = TRUE)
# long distance 50
c7 = c(c7,sample(1:5, 50, replace = TRUE))
c8 = c(c8,sample(20:25, 50, replace = TRUE))
c13 = c(c13,sample(60:70, 50, replace = TRUE))
c14 = c(c14,sample(80:83, 50, replace = TRUE))
# inter RNA 100
c7 = c(c7,sample(1:5, 100, replace = TRUE))
c8 = c(c8,sample(20:25, 100, replace = TRUE))
c13 = c(c13,sample(1:5, 100, replace = TRUE))
c14 = c(c14,sample(20:25, 100, replace = TRUE))
exampleInput = data.frame(V1 = c1,
V2 = c2,
V3 = c3,
V4 = c4,
V5 = as.numeric(c5),
V6 = as.numeric(c6),
V7 = as.numeric(c7),
V8 = as.numeric(c8),
V9 = c9,
V10 = c10,
V11 = as.numeric(c11),
V12 = as.numeric(c12),
V13 = as.numeric(c13),
V14 = as.numeric(c14),
V15 = c15)
file = tempfile()
write.table(exampleInput,
file = file,
quote = FALSE,
row.names = FALSE,
sep = "\t", col.names = F)
c4 = c(rep("transcript1",55),rep("transcript2",90) )
c10 = c(rep("transcript1",145) )
c1 = 1:145
c2 = rep(paste(rep("A", 40), collapse = ""),145)
c3 = rep(".",145)
c9 = rep(".",145)
c15 = rep(".",145)
c5 = rep(1,145)
c11 = rep(21,145)
c6 = rep(20,145)
c12= rep(40,145)
# short distance 55
c7 = sample(1:5, 55, replace = TRUE)
c8 = sample(20:25, 55, replace = TRUE)
c13 = sample(20:25, 55, replace = TRUE)
c14 = sample(40:45, 55, replace = TRUE)
# inter RNA 100
c7 = c(c7,sample(1:40, 90, replace = TRUE))
c8 = c(c8,sample(20:75, 90, replace = TRUE))
c13 = c(c13,sample(1:40, 90, replace = TRUE))
c14 = c(c14,sample(20:75, 90, replace = TRUE))
exampleInput = data.frame(V1 = c1,
V2 = c2,
V3 = c3,
V4 = c4,
V5 = as.numeric(c5),
V6 = as.numeric(c6),
V7 = as.numeric(c7),
V8 = as.numeric(c8),
V9 = c9,
V10 = c10,
V11 = as.numeric(c11),
V12 = as.numeric(c12),
V13 = as.numeric(c13),
V14 = as.numeric(c14),
V15 = c15)
file2 = tempfile()
write.table(exampleInput,
file = file2,
quote = FALSE,
row.names = FALSE,
sep = "\t",
col.names = F)
# Set up the sample table. ----
sampleTabler1 = c(file, "s", "1", "s1")
sampleTabler2 = c(file2, "c", "1", "c1")
# make the sample table
sampleTable2 = rbind.data.frame(sampleTabler1, sampleTabler2)
# add the column names
colnames(sampleTable2) = c("file", "group", "sample", "sampleName")
# Choose RNA and set up the object ----
rna = c("transcript1")
# load the object
cds = rnaCrosslinkDataSet(rnas = rna,
rnaSize = 0,
sampleTable = sampleTable2)
return(cds)
}
JLP-BioInf/comradesOO documentation built on April 28, 2024, 4:22 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions makeExamplernaCrosslinkDataSet swapInputs3 swapInputs2 swapInputs
Documented in makeExamplernaCrosslinkDataSet swapInputs swapInputs2 swapInputs3
#' @include rnaCrosslinkDataSet.R
NULL
################################################################################
# Methods functions and methods relating to creation and use of
# rnaCrosslinkDataSets
################################################################################
#' featureInfo
#'
#' Produces a list list of 2 elemnts 'transcript' and 'family'
#' Each element contains a table with the counts for each RNA in each sample that
#' interact with the target RNA
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#'
#'
#' @name featureInfo
#' @docType methods
#' @aliases featureInfo,rnaCrosslinkDataSet-method
#' @rdname featureInfo
#' @return A list - Feature level and transcript level counts for each sample
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' featureInfo(cds)
#'
#' @export
setGeneric("featureInfo",
function(cds)
standardGeneric("featureInfo"))
setMethod("featureInfo",
"rnaCrosslinkDataSet",
function(cds) {
alteredInputList = list()
TE = rnas(cds)
InputList = getData(x = cds,
data = "InputFiles",
type = "original")
for (Input in 1:length(InputList)) {
controlInput = InputList[[Input]]
#get the right columns
controlInput = as.data.frame(cbind(
as.character(controlInput$V4),
as.character(controlInput$V10)
))
#get only those lines with the RNA
controlInput = controlInput
#remove the factor levels
controlInput$V1 = as.character(controlInput$V1)
controlInput$V2 = as.character(controlInput$V2)
#Swap the columns around
controlInputtmp1 = controlInput[controlInput$V1 == TE, ]
controlInputtmp2 = controlInput[!(controlInput$V1 == TE), ]
controlInputtmp2 = rev(controlInputtmp2)
colnames(controlInputtmp2) = c("V1", "V2")
controlInput = as.data.frame(rbind(controlInputtmp1, controlInputtmp2))
#add to list
alteredInputList[[TE]][[Input]] = controlInput
}
##############################
# Now Combine the two
##############################
df = list()
df[[TE]] = data.frame()
aggList = list()
totalNames = c()
for (Input in 1:length(alteredInputList[[TE]])) {
sampleInput = alteredInputList[[TE]][[Input]]
sData = sampleInput[sampleInput$V1 == TE, ]
freqSample2 = aggregate(sData$V1, by = list(sData$V2), FUN = length)
freqSample = freqSample2$x
names(freqSample) = freqSample2$Group.1
aggList[[TE]][[Input]] = freqSample
# Get the total features that exist in the dataset
totalNames = unique(sort(c(totalNames, names(freqSample))))
}
# geta. matrix for plotting
tmpMat = matrix(0, nrow = length(totalNames), ncol = length(InputList))
row.names(tmpMat) = totalNames
colnames(tmpMat) = sampleNames(cds)
for (i in 1:nrow(tmpMat)) {
for (j in 1:ncol(tmpMat)) {
tmpMat[i, j] = aggList[[TE]][[j]][row.names(tmpMat)[i]]
}
}
tmpMat[is.na(tmpMat)] <- 0.00001
df[[TE]] = as.data.frame(tmpMat)
# now make a stats list
statsList = list()
statsList[[TE]] = df[[TE]]
statsList[[TE]]$ID = sapply(row.names(statsList[[TE]]), function(x)
tail(strsplit(x, "_")[[1]],n=1), USE.NAMES = FALSE)
statsList[[TE]]
# Now get the stats from aggregate
aggList = list()
aggList2 = list()
aggList[[TE]] = aggregate(. ~ ID , statsList[[TE]], sum)
#get sample Table
st = sampleTable(cds)
#find out which samples have controls
samples = st[which(duplicated(st$sample)), "sample"]
for (i in samples) {
# now get the control and sample index
control = as.numeric(which(st$sample == i &
st$group == "c")) + 1
sample = as.numeric(which(st$sample == i &
st$group == "s")) + 1
factor1 = sum(aggList[[TE]][, sample]) / sum(aggList[[TE]][, control])
aggList[[TE]][, paste("norm", i, sep = "_")] = aggList[[TE]][, sample] / (aggList[[TE]][, control] * factor1)
}
data = melt(aggList[[TE]], id.vars = c("ID"))
samples = paste("norm", samples, sep = "_")
plot(ggplot() +
geom_boxplot(data = data[data$variable %in% samples, ], aes(
x = reorder(data[data$variable %in% samples, ]$ID,
data[data$variable %in% samples, ]$value,
FUN = median), y = log2(value)
)) +
geom_point(data = data[data$variable %in% samples, ], aes(
x = reorder(data[data$variable %in% samples, ]$ID,
data[data$variable %in% samples, ]$value,
FUN = median),
y = log2(value),
fill = ID
)) +
geom_hline(yintercept = 0, colour = "darkred") +
theme_classic() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) )
featureStats = list()
featureStats[["transcript"]] = df[[TE]]
featureStats[["family"]] = aggList[[TE]]
return(featureStats)
})
#' topInteractions
#'
#' This method prints the top transcript interactions that have the most duplexes
#' assigned
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#' @param ntop the number of entries to display
#'
#' @name topInteractions
#' @docType methods
#' @rdname topInteractions
#' @aliases topInteractions,rnaCrosslinkDataSet-method
#' @return A table, the number of counts per sample per interaction
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' topInteractions(cds)
#'
#' @export
setGeneric("topInteractions",
function(cds,
ntop = 10)
standardGeneric("topInteractions"))
setMethod("topInteractions",
"rnaCrosslinkDataSet",
function(cds,
ntop = 10) {
ci = group(cds)[["c"]]
si = group(cds)[["s"]]
df= data.frame()
for (i in names(InputFiles(cds)[["all"]][["all"]])) {
same = which(InputFiles(cds)[["all"]][["all"]][[i]]$V4 ==
InputFiles(cds)[["all"]][["all"]][[i]]$V10 )
notSame = which(InputFiles(cds)[["all"]][["all"]][[i]]$V4 !=
InputFiles(cds)[["all"]][["all"]][[i]]$V10)
s = paste(InputFiles(cds)[["all"]][["all"]][[i]]$V4[same],
InputFiles(cds)[["all"]][["all"]][[i]]$V10[same], sep = "::")
#s = unlist(lapply(s, function(x)
# unlist(strsplit(x,split = "::"))[1] ))
s = as.data.frame(table(s))
s$type="intra"
s$sample = i
colnames(s) = c("RNA","reads","type","sample")
t = paste(InputFiles(cds)[["all"]][["all"]][[i]]$V4[notSame],
InputFiles(cds)[["all"]][["all"]][[i]]$V10[notSame], sep = "::")
#t1 = unlist(lapply(t, function(x)
# unlist(strsplit(x,split = "::"))[1] ))
#t2 = unlist(lapply(t, function(x)
# unlist(strsplit(x,split = "::"))[2] ))
#t = c(t1,t2)
t = as.data.frame(table(t))
t$type="inter"
t$sample = i
colnames(t) = c("RNA","reads","type","sample")
df =rbind.data.frame(df,t,s)
}
df = dcast(df,formula = RNA + type ~ sample, value.var = "reads")
ci = sampleTable(cds)[ci,"sampleName"]
si = sampleTable(cds)[si,"sampleName"]
if(length(si) == 1){
df$samples = df[,si]
df$control = df[,ci]
df$enrichment = df$samples / df$control
}else{
df$samples = rowSums(df[,si],na.rm = TRUE)
df$control = rowSums(df[,ci],na.rm = TRUE)
df$enrichment = df$samples / df$control
}
df = df[order(df$samples,decreasing = TRUE),]
df = df[1:ntop,]
df
})
#' topTranscripts
#'
#' This method prints the top transcripts that have the most duplexes
#' assigned
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#' @param ntop the number of entries to display
#'
#' @name topTranscripts
#' @docType methods
#' @rdname topTranscripts
#' @aliases topTranscripts,rnaCrosslinkDataSet-method
#' @return A table, the number of counts per sample per transcript
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' topTranscripts(cds)
#' @export
#'
setGeneric("topTranscripts",
function(cds,
ntop = 10)
standardGeneric("topTranscripts"))
setMethod("topTranscripts",
"rnaCrosslinkDataSet",
function(cds,
ntop = 10) {
ci = group(cds)[["c"]]
si = group(cds)[["s"]]
df= data.frame()
for (i in names(InputFiles(cds)[["all"]][["all"]])) {
same = which(InputFiles(cds)[["all"]][["all"]][[i]]$V4 ==
InputFiles(cds)[["all"]][["all"]][[i]]$V10 )
notSame = which(InputFiles(cds)[["all"]][["all"]][[i]]$V4 !=
InputFiles(cds)[["all"]][["all"]][[i]]$V10)
s = paste(InputFiles(cds)[["all"]][["all"]][[i]]$V4[same],
InputFiles(cds)[["all"]][["all"]][[i]]$V10[same], sep = "::")
s = unlist(lapply(s, function(x)
unlist(strsplit(x,split = "::"))[1] ))
s = as.data.frame(table(s))
s$type="intra"
s$sample = i
colnames(s) = c("RNA","reads","type","sample")
t = paste(InputFiles(cds)[["all"]][["all"]][[i]]$V4[notSame],
InputFiles(cds)[["all"]][["all"]][[i]]$V10[notSame], sep = "::")
t1 = unlist(lapply(t, function(x)
unlist(strsplit(x,split = "::"))[1] ))
t2 = unlist(lapply(t, function(x)
unlist(strsplit(x,split = "::"))[2] ))
t = c(t1,t2)
t = as.data.frame(table(t))
t$type="inter"
t$sample = i
colnames(t) = c("RNA","reads","type","sample")
df =rbind.data.frame(df,t,s)
}
df = dcast(df,formula = RNA + type ~ sample, value.var = "reads")
ci = sampleTable(cds)[ci,"sampleName"]
si = sampleTable(cds)[si,"sampleName"]
if(length(si) == 1){
df$samples = df[,si]
df$control = df[,ci]
df$enrichment = df$samples / df$control
}else{
df$samples = rowSums(df[,si],na.rm = TRUE)
df$control = rowSums(df[,ci],na.rm = TRUE)
df$enrichment = df$samples / df$control
}
df = df[order(df$samples,decreasing = TRUE),]
df = df[1:ntop,]
df
})
#' topInteracters
#'
#' This method prints the top transcripts that have the most duplexes
#' assigned that interact with the transcript of interest
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#' @param ntop the number of entries to display
#' @param sds known bug, doesn't work for small data sets fix incoming
#'
#' @name topInteracters
#' @docType methods
#' @rdname topInteracters
#' @aliases topInteracters,rnaCrosslinkDataSet-method
#' @return A table, the number of counts per sample per interacting transcript
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' topInteracters(cds, sds = TRUE)
#'
#' @export
setGeneric("topInteracters",
function(cds,
ntop = 10,
sds = TRUE)
standardGeneric("topInteracters"))
setMethod("topInteracters",
"rnaCrosslinkDataSet",
function(cds,
ntop = 10,
sds = TRUE) {
if(sds != TRUE){
ci = group(cds)[["c"]]
si = group(cds)[["s"]]
df= data.frame()
for (i in names(InputFiles(cds)[[rnas(cds)]][["noHost"]])) {
iFile = InputFiles(cds)[[rnas(cds)]][["noHost"]][[i]]
same = which(iFile$V4 == iFile$V10 )
notSame = which(iFile$V4 != iFile$V10 )
s = iFile$V4[same]
s = as.data.frame(table(s))
s$type="intra"
s$sample = i
colnames(s) = c("RNA","reads","type","sample")
v4rna = which(iFile$V4[notSame] == rnas(cds))
t1 = iFile$V4[notSame][-v4rna]
t2 = iFile$V10[notSame][v4rna]
t = c(t1,t2)
if(length(notSame) == 0){
df =rbind.data.frame(df,t,c(NA,NA, "inter", i),stringsAsFactors = F)
}else{
t = as.data.frame(table(t),stringsAsFactors = F)
t$type="inter"
t$sample = i
colnames(t) = c("RNA","reads","type","sample")
}
if(length(same) == 0){ df =rbind.data.frame(df,s,c(NA,NA, "inter", i),stringsAsFactors = F)
}else{
df =rbind.data.frame(df,t,s,stringsAsFactors = F)
}
}
df$reads = as.numeric(df$reads)
df = dcast(df,formula = RNA + type ~ sample, value.var = "reads")
ci = sampleTable(cds)[ci,"sampleName"]
si = sampleTable(cds)[si,"sampleName"]
if(length(si) == 1){
df$samples = df[,si]
df$control = df[,ci]
df$enrichment = df$samples / df$control
}else{
df$samples = rowSums(df[,si],na.rm = TRUE)
df$control = rowSums(df[,ci],na.rm = TRUE)
df$enrichment = df$samples / df$control
}
df = df[order(df$samples,decreasing = TRUE),]
df = df[1:ntop,]
df
}
})
#' getInteractions
#'
#' This method returns a table of interactions of an RNA (interactor) on the RNA of interest.
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#' @param interactors A vector containing the names of RNAs to show interactions with
#'
#'
#' @name getInteractions
#' @docType methods
#' @rdname getInteractions
#' @return A table showing the read coverage of the specified interacting RNAs
#' @aliases getInteractions,rnaCrosslinkDataSet-method
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' getInteractions(cds, c("transcript1","transcript2"))
#'
#' @export
setGeneric("getInteractions",
function(cds,
interactors)
standardGeneric("getInteractions"))
setMethod("getInteractions",
"rnaCrosslinkDataSet",
function(cds,
interactors) {
seq2 <- Vectorize(seq.default, vectorize.args = c("from", "to"))
# subset the Input files based on the interactor of choice
table = data.frame()
for (interactor in interactors){
for (i in names(InputFiles(cds)[[rnas(cds)]][["host"]])) {
x = InputFiles(cds)[[rnas(cds)]][["host"]][[i]]
x = x[x$V10 == interactor, ]
if (nrow(x) == 0) {
v = as.data.frame(0)
}
else {
starts = x$V7
ends = x$V8
v = as.data.frame(unlist(seq2(from = starts, to = ends)))
if(ncol(v) > 1){
next
}
}
v$rna = interactor
v$sample = i
colnames(v) = c("Position", "rna", "sample")
table = rbind.data.frame(table, v)
}
}
colnames(table) = c("Position", "rna", "sample")
table = aggregate(
table$Position,
by = list(table$rna,
table$sample,
table$Position),
FUN = length
)
colnames(table) = c("rna", "sample", "Position", "depth")
table
})
#' getReverseInteractions
#'
#' This method prints interactions of the RNA of interest on another RNA
#' transcript.
#'
#' @param cds a \code{rnaCrosslinkDataSet} object
#' @param interactor The rna to show interactions with
#'
#' @name getReverseInteractions
#' @docType methods
#' @rdname getReverseInteractions
#' @aliases getReverseInteractions,rnaCrosslinkDataSet-method
#' @return A long format table shoing the read coverage of chosen RNA
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' getReverseInteractions(cds, 'transcript2')
#'
#' @export
setGeneric("getReverseInteractions",
function(cds,
interactor)
standardGeneric("getReverseInteractions"))
setMethod("getReverseInteractions",
"rnaCrosslinkDataSet",
function(cds,
interactor) {
seq2 <- Vectorize(seq.default, vectorize.args = c("from", "to"))
# subset the Input files based on the interacter of choice
table = data.frame()
for (i in names(InputFiles(cds)[[rnas(cds)]][["host"]])) {
x = InputFiles(cds)[[rnas(cds)]][["host"]][[i]]
x = x[x$V10 == interactor, ]
if (nrow(x) == 0) {
v = as.data.frame(0)
v$rna = interactor
v$sample = i
colnames(v) = c("Position", "rna", "sample")
table = rbind.data.frame(table, v)
} else {
starts = x$V13
ends = x$V14
v = as.data.frame(unlist(seq2(from = starts, to = ends)))
v$rna = interactor
v$sample = i
colnames(v) = c("Position", "rna", "sample")
table = rbind.data.frame(table, v)
}
}
colnames(table) = c("Position", "rna", "sample")
table = aggregate(
table$Position,
by = list(table$rna,
table$sample,
table$Position),
FUN = length
)
colnames(table) = c("rna", "sample", "Position", "depth")
table
})
#' swapInputs
#'
#' Swap the table to ensure that 3 prime most duplex side is on he left of the
#' table used to make one sides heatmaps and other reasons where having the
#' left of the table coming after the right side is a problem. Different from
#' swapInputs as it ensure that BOTH duplex sides originate from the RNA of
#' interest.
#'
#' @param InputList the original InputList created with readInputFiles or subsetInputList
#' @param rna The rna of interest
#'
#' @name swapInputs
#' @docType methods
#' @rdname swapInputs
#' @return A list of "swapped" Input datas
#'
swapInputs = function(InputList,
rna) {
InputListSwap = InputList
for (Input in 1:length(InputList)) {
InputListS = InputList[[Input]][InputList[[Input]]$V4 == rna |
InputList[[Input]]$V10 == rna, ]
comb = InputListS
InputListSwap[[Input]] = comb
}
return(InputListSwap)
}
#' swapInputs2
#'
#' Swap the table to ensure that 3 prime most duplex side is on the left of the table
#' used to make one sides heatmaps and other reasons where having the left of the table
#' coming after the right side is a problem. Different from swapInputs as it
#' ensure that BOTH duplex sides originate from the RNA of interest.
#' @param InputList the original InputList created with readInputFiles or subsetInputList
#' @param rna The rna of interest
#' @name swapInputs2
#' @docType methods
#' @rdname swapInputs2
#' @return A list of "swapped" Input data
#'
swapInputs2 = function(InputList,
rna) {
for (Input in 1:length(InputList)) {
InputList18S = InputList[[Input]][as.character(InputList[[Input]]$V4) == rna &
as.character(InputList[[Input]]$V10) == rna, ]
tmp1 = InputList18S[InputList18S$V7 < InputList18S$V13, ]
tmp2 = InputList18S[InputList18S$V7 > InputList18S$V13, ]
tmp2 = tmp2[, c(
"V1",
"V2",
"V3",
"V4",
"V11",
"V12",
"V13",
"V14",
"V15",
"V10",
"V5",
"V6",
"V7",
"V8",
"V9"
)]
colnames(tmp2) = colnames(tmp1)
comb = rbind.data.frame(tmp1, tmp2)
InputList[[Input]] = comb
}
return(InputList)
}
#' swapInputs3
#'
#' Swap the table to ensure that 3 prime most duplex side is ont he left of the table
#' used to make one sides heatmaps and other reasons where having the left of the table
#' coming after the right side is a problem. Different from swapInputs as it
#' ensure that BOTH duplex sides originate from the RNA of interest.
#' @param InputList the original InputList created with readInputFiles or subsetInputList
#' @param rna The rna of interest
#'
#' @return A list of "swapped" Input datas
#' @name swapInputs3
#' @docType methods
#' @rdname swapInputs3
swapInputs3 = function(InputList, rna) {
InputListSwap = InputList
for (Input in 1:length(InputList)) {
InputListS = InputList[[Input]][InputList[[Input]]$V4 == rna |
InputList[[Input]]$V10 == rna, ]
tmp1 = InputListS[InputListS$V4 == rna, ]
tmp2 = InputListS[InputListS$V4 != rna, ]
tmp2 = tmp2[, c(
"V1",
"V2",
"V3",
"V10",
"V11",
"V12",
"V13",
"V14",
"V15",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9"
)]
colnames(tmp2) = colnames(tmp1)
comb = rbind.data.frame(tmp1, tmp2, stringsAsFactors = F)
InputListSwap[[Input]] = comb
}
return(InputListSwap)
}
#' makeExamplernaCrosslinkDataSet
#'
#' Creat a minimal example rnaCrosslinkdataSetObject
#'
#' @return An example rnaCrosslinkDataSet objext
#' @name makeExamplernaCrosslinkDataSet
#' @rdname makeExamplernaCrosslinkDataSet
#' @examples
#' cds = makeExamplernaCrosslinkDataSet()
#' @export
makeExamplernaCrosslinkDataSet = function() {
c4 = c(rep("transcript1",100),rep("transcript2",100) )
c10 = c(rep("transcript1",200) )
c1 = 1:200
c2 = rep(paste(rep("A", 40), collapse = ""),200)
c3 = rep(".",200)
c9 = rep(".",200)
c15 = rep(".",200)
c5 = rep(1,200)
c11 = rep(21,200)
c6 = rep(20,200)
c12= rep(40,200)
# short distance 50
c7 = sample(1:5, 50, replace = TRUE)
c8 = sample(20:25, 50, replace = TRUE)
c13 = sample(20:25, 50, replace = TRUE)
c14 = sample(40:45, 50, replace = TRUE)
# long distance 50
c7 = c(c7,sample(1:5, 50, replace = TRUE))
c8 = c(c8,sample(20:25, 50, replace = TRUE))
c13 = c(c13,sample(60:70, 50, replace = TRUE))
c14 = c(c14,sample(80:83, 50, replace = TRUE))
# inter RNA 100
c7 = c(c7,sample(1:5, 100, replace = TRUE))
c8 = c(c8,sample(20:25, 100, replace = TRUE))
c13 = c(c13,sample(1:5, 100, replace = TRUE))
c14 = c(c14,sample(20:25, 100, replace = TRUE))
exampleInput = data.frame(V1 = c1,
V2 = c2,
V3 = c3,
V4 = c4,
V5 = as.numeric(c5),
V6 = as.numeric(c6),
V7 = as.numeric(c7),
V8 = as.numeric(c8),
V9 = c9,
V10 = c10,
V11 = as.numeric(c11),
V12 = as.numeric(c12),
V13 = as.numeric(c13),
V14 = as.numeric(c14),
V15 = c15)
file = tempfile()
write.table(exampleInput,
file = file,
quote = FALSE,
row.names = FALSE,
sep = "\t", col.names = F)
c4 = c(rep("transcript1",55),rep("transcript2",90) )
c10 = c(rep("transcript1",145) )
c1 = 1:145
c2 = rep(paste(rep("A", 40), collapse = ""),145)
c3 = rep(".",145)
c9 = rep(".",145)
c15 = rep(".",145)
c5 = rep(1,145)
c11 = rep(21,145)
c6 = rep(20,145)
c12= rep(40,145)
# short distance 55
c7 = sample(1:5, 55, replace = TRUE)
c8 = sample(20:25, 55, replace = TRUE)
c13 = sample(20:25, 55, replace = TRUE)
c14 = sample(40:45, 55, replace = TRUE)
# inter RNA 100
c7 = c(c7,sample(1:40, 90, replace = TRUE))
c8 = c(c8,sample(20:75, 90, replace = TRUE))
c13 = c(c13,sample(1:40, 90, replace = TRUE))
c14 = c(c14,sample(20:75, 90, replace = TRUE))
exampleInput = data.frame(V1 = c1,
V2 = c2,
V3 = c3,
V4 = c4,
V5 = as.numeric(c5),
V6 = as.numeric(c6),
V7 = as.numeric(c7),
V8 = as.numeric(c8),
V9 = c9,
V10 = c10,
V11 = as.numeric(c11),
V12 = as.numeric(c12),
V13 = as.numeric(c13),
V14 = as.numeric(c14),
V15 = c15)
file2 = tempfile()
write.table(exampleInput,
file = file2,
quote = FALSE,
row.names = FALSE,
sep = "\t",
col.names = F)
# Set up the sample table. ----
sampleTabler1 = c(file, "s", "1", "s1")
sampleTabler2 = c(file2, "c", "1", "c1")
# make the sample table
sampleTable2 = rbind.data.frame(sampleTabler1, sampleTabler2)
# add the column names
colnames(sampleTable2) = c("file", "group", "sample", "sampleName")
# Choose RNA and set up the object ----
rna = c("transcript1")
# load the object
cds = rnaCrosslinkDataSet(rnas = rna,
rnaSize = 0,
sampleTable = sampleTable2)
return(cds)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.