Type: Package
Title: Analysis of RNA Crosslinking Data
Version: 0.1.4
Maintainer: Jonathan Price <jlp76@cam.ac.uk>
Description: Analysis of RNA crosslinking data for RNA structure prediction. The package is suitable for the analysis of RNA structure cross-linking data and chemical probing data.
License: GPL-3
Encoding: UTF-8
BugReports: https://github.com/JLP-BioInf/rnaCrosslinkOO/issues
Depends: seqinr, GenomicRanges, stats
Imports: ggplot2, reshape2, MASS, mixtools, utils, S4Vectors, patchwork, doParallel, igraph, R4RNA, RColorBrewer, IRanges, foreach,grDevices, heatmap3, TopDom, tidyverse, RRNA, ggrepel, methods, parallel, ClassDiscovery
RoxygenNote: 7.3.1
Collate: 'rnaCrosslinkOO.R' 'rnaCrosslinkDataSet.R' 'clusterrnaCrosslink.R' 'clusterrnaCrosslinkMethodsAndHelpers.R' 'commonHelpersAndMethods.R' 'commonStatsAndPlots.R' 'foldrnaCrosslink.R' 'foldrnaCrosslinkMethodsAndHelpers.R' 'genericMethods.R' 'rnaCrosslinkDataSetMethodsAndHelpers.R' 'rnaCrosslinkOO-package.R' 'rnaCrosslinkQC.R'
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2024-07-10 15:59:22 UTC; jp
Author: Jonathan Price ORCID iD [aut, cre], Andrew Lim [ctb]
Repository: CRAN
Date/Publication: 2024-07-10 23:40:02 UTC

InputFiles

Description

Extract the data in original format

Usage

InputFiles(x)

Arguments

x

A rnaCrosslinkDataSet object

Value

A list of tables in the original input format, one entry for each sample

Examples

cds = makeExamplernaCrosslinkDataSet()

InputFiles(cds)

InputToGRanges

Description

This function is useful to turn a list of Input data into lists of GRanges It creates a list for each sample one for the left side one for the right side and one for the gap in the middle.

Usage

InputToGRanges(InputList, rna)

Arguments

InputList

the original InputList created with readInputFiles or subsetInputList

rna

The rna of interest

Value

A list of GRanges data in Input format

clusterGrangesList

Description

Extract the cluster coordinates in granges format

Usage

clusterGrangesList(x)

Arguments

x

A rnaCrosslinkDataSet object

Value

A list of Granges objects showing the positions of each cluster, one entry for each sample

Examples

cds = makeExamplernaCrosslinkDataSet()

clusterGrangesList(cds)

clusterGrangesList<-

Description

Set new clusterGrangesList slot

Usage

clusterGrangesList(x) <- value

Arguments

x

A rnaCrosslinkDataSet object

value

A replacement

Value

No return - Sets a new clusterGrangesList slot

Examples

cds = makeExamplernaCrosslinkDataSet()

newclusterGrangesList <- clusterGrangesList(cds)
clusterGrangesList(cds) <- newclusterGrangesList

clusterNumbers

Description

This method prints a table showing the number of clusters in each step of the analysis

Usage

clusterNumbers(knowClusteredCds, rna)

Arguments

knowClusteredCds

A rnaCrosslinkDataSet object after clustering has been performed

rna

The RNA ID of interest - use rna(cdsObject).

Value

A data.frame shoing the number of clusters for each sample

Examples

cds = makeExamplernaCrosslinkDataSet()

clusteredCds = clusterrnaCrosslink(cds,
                cores = 1,
                stepCount = 1,
                clusterCutoff = 1)
clusterNumbers(clusteredCds)

clusterTableFolded

Description

Extract the cluster coordinates with fold prediciton in data frame format

Usage

clusterTableFolded(x)

Arguments

x

A rnaCrosslinkDataSet object

Value

A table showing the vienna structures of each cluster

Examples

cds = makeExamplernaCrosslinkDataSet()

clusterTableFolded(cds)

clusterTableList

Description

Extract the cluster coordinates in data frame format

Usage

clusterTableList(x)

Arguments

x

A rnaCrosslinkDataSet object

Value

A list of tables showing the vienna structures of each cluster

Examples

cds = makeExamplernaCrosslinkDataSet()

clusterTableList(cds)

clusterTableList<-

Description

Set new clusterTableList slot

Usage

clusterTableList(x) <- value

Arguments

x

A rnaCrosslinkDataSet object

value

A replacement

Value

No return - Sets a new clusterTableList slot

Examples

cds = makeExamplernaCrosslinkDataSet()

newclusterGrangesList <- clusterTableList(cds)
clusterTableList(cds) <- newclusterGrangesList

Description

This method clusters the duplexes.

Usage

clusterrnaCrosslink(cds, cores = 3, stepCount = 2, clusterCutoff = 20)

Arguments

cds

rnaCrosslinkDataSet object created with rnaCrosslinkDataSet

cores

numeric - The number of cores to use

stepCount

Stringency for clustering

clusterCutoff

The minimum number of reads a cluster requires

Value

A rnaCrosslinkDataSet object

Examples

cds = makeExamplernaCrosslinkDataSet()

clusterrnaCrosslink(cds,
                cores = 1,
                stepCount = 1,
                clusterCutoff = 0)

compareKnown

Description

This method compares the current object to a know structure.run trimClusters() on the rnaCrosslinkDataSet first

Usage

compareKnown(trimmedClusters, knownMat, type)

Arguments

trimmedClusters

a rnaCrosslinkDataSet object, run trimClusters() on the rnaCrosslinkDataSet first

knownMat

Matrix - A marix(ncol = lengthRNA,nrow = lengthRNA) where a value in matrix[x,y] would indicate a known interation between nucleotide x and nucleotide y

type

string - the Analysis stage of clusters you would like to compare you can find available types by just running the objects name

Value

Returns a rnaCrosslinkClusteredDataSet object

The 3 attributes matrixList, clusterTableList and clusterGrangesList will gain the types "known" and "novel" and "knownAndNovel"

Examples

cds = makeExamplernaCrosslinkDataSet()

clusteredCds = clusterrnaCrosslink(cds,
                cores = 1,
                stepCount = 1,
                clusterCutoff = 0)
knownMat = matrix(0, ncol = rnaSize(cds), nrow = rnaSize(cds))
knownMat[7,27] = 1
# use compare known to gett he known and not know clusters
knowClusteredCds = compareKnown(clusteredCds,
                                knownMat,
                                "original")
clusterNumbers(knowClusteredCds)

compareKnownStructures

Description

This method compares the predicted structures to a set of known interactions

Usage

compareKnownStructures(foldedCds, file)

Arguments

foldedCds

rnaCrosslinkDataSet after running foldrnaCrosslink

file

a two column file with column header i and j with numeric values showing nucleoide i binds to nucleotide j

Value

Returns a dataframe

a tables showing the number of predicted interactions and their agreement

Examples

## Not run: 
cds = makeExamplernaCrosslinkDataSet()
clusteredCds = clusterrnaCrosslink(cds = cds,
                               cores = 3,
                               stepCount = 2,
                               clusterCutoff = 1)


trimmedClusters = trimClusters(clusteredCds = clusteredCds,trimFactor = 1, clusterCutoff = 1)



fasta = paste(c(rep('A',25),
                rep('T',25),
                rep('A',10),
                rep('T',23)),collapse = "")

header = '>transcript1'


fastaFile = tempfile()
writeLines(paste(header,fasta,sep = "\n"),con = fastaFile)


rnaRefs = list()
rnaRefs[[rnas(cds)]] = read.fasta(fastaFile)
rnaRefs



foldedCds = foldrnaCrosslink(trimmedClusters,
                         rnaRefs = rnaRefs,
                         start = 1,
                         end = 83,
                         shape = 0,
                         ensembl = 5,
                         constraintNumber  = 1,
                         evCutoff = 1)


# make an example table of "know" interactions
file = data.frame(V1 = c(6), 
                  V2 = c(80))
compareKnownStructures(foldedCds,file)

## End(Not run)

featureInfo

Description

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

Usage

featureInfo(cds)

Arguments

cds

a rnaCrosslinkDataSet object

Value

A list - Feature level and transcript level counts for each sample

Examples

cds = makeExamplernaCrosslinkDataSet()
featureInfo(cds)

findBasePairsRNAcoFold2

Description

Folds the clusters using Vienna RNAfold

Usage

findBasePairsRNAcoFold2(
  startPos1,
  endPos1,
  seq1,
  startPos2,
  endPos2,
  seq2,
  fasta,
  shape
)

Arguments

startPos1

Start of the cluster side x

endPos1

End of the cluster side x

seq1

Sequence of x

startPos2

Start of the cluster side y

endPos2

End of the cluster side y

seq2

Sequence of y

fasta

rnaRefs

shape

shape reactivities

Value

A table of clusters and coordinates with folds

findBasePairsRNAfold

Description

Folds the clusters using Vienna RNA duplex

Usage

findBasePairsRNAfold(startPos, endPos, seqs, fasta, shape)

Arguments

startPos

Start of the cluster side x

endPos

End of the cluster side x

seqs

Sequence of x

fasta

rnaRefs

shape

shape reactivities

Value

A table of clusters and coordinates with folds

findBasePairsRNAfold2

Description

Folds the clusters using Vienna RNA duplex

Usage

findBasePairsRNAfold2(startPos, endPos, seqs, fasta)

Arguments

startPos

Start of the cluster side x

endPos

End of the cluster side x

seqs

Sequence of x

fasta

rnaRefs

Value

A table of clusters and coordinates with folds

Description

This methods folds an ensebl of structures for the whole RNA or chosen region of the RNA. See rnaCrosslinkDataSet for slot information.

Usage

foldrnaCrosslink(
  cdsObject,
  rnaRefs,
  start,
  end,
  evCutoff = 1,
  ensembl = 50,
  constraintNumber = 20,
  shape = 0
)

Arguments

cdsObject

rnaCrosslinkDataSet object created with rnaCrosslinkDataSet

rnaRefs

named List - a list with named elements that correspond to the .RNA of interest. The element of the list must be a fasta file that has been read with seqinr::read.fasta()

start

Start of segmnent to fold

end

End of segmnent to fold

evCutoff

Mininum number of read support for contraint to be included in folding

ensembl

Number of structures to Nake

constraintNumber

Number of constraints to add to each final fold

shape

shape reactivities (0 for no constraints)

Value

a rnaCrosslinkDataSet object

Examples

## Not run: 
cds = makeExamplernaCrosslinkDataSet()

clusteredCds = clusterrnaCrosslink(cds,
                cores = 1,
                stepCount = 1,
                clusterCutoff = 0)
                
trimmedClusters = trimClusters(clusteredCds = clusteredCds,
             trimFactor = 1, 
             clusterCutoff = 0)
             
              

fasta = paste(c(rep('A',25),
                rep('T',25),
                rep('A',10),
                rep('T',23)),collapse = "")

header = '>transcript1'


fastaFile = tempfile()
writeLines(paste(header,fasta,sep = "\n"),con = fastaFile)


rnaRefs = list()
rnaRefs[[rnas(cds)]] = read.fasta(fastaFile)
rnaRefs



foldedCds = foldrnaCrosslink(trimmedClusters,
                         rnaRefs = rnaRefs,
                         start = 1,
                         end = 83,
                         shape = 0,
                         ensembl = 5,
                         constraintNumber  = 1,
                         evCutoff = 1)
foldedCds

## End(Not run)

getAdjacancyMat

Description

Makes and adjacency matrix list (for clustering)

Usage

getAdjacancyMat(InputGranges, nucletideOrPerc, cutoff)

Arguments

InputGranges

list created with InputToGRanges (but just the gap section of the list)

nucletideOrPerc

measure difference by percentage or nucleotides

cutoff

The maximum difference before giving these two gaps 0

Details

Makes and adjacency matrix list (for clustering)

Value

A list of Adjacancy matrices

getClusterClusterShortRangeWhole

Description

Decides if a cluster is long or short range then either grabs the whole sequence or the sequence of the two sides of the interaction separately.

Usage

getClusterClusterShortRangeWhole(cluster, seq)

Arguments

cluster

cluster positions

seq

sequence of transcript

Value

The same table with an extra column

getData

Description

Get data is more generic method for retrieving data from the object and returns a list, the number of entries in the list is number of samples in the dataset and the list contain entries of the data type and analysis stage you select.

Usage

getData(x, data, type)

Arguments

x

A rnaCrosslinkDataSet object

data

The data type to return <InputFiles | matrixList | clusterGrangesList | clusterTableList>

type

The analysis stage <original | noHost | originalClusters | trimmedClusters>

Value

A list of the chosen data type - one entry for each sample

Examples

cds = makeExamplernaCrosslinkDataSet()

getData(cds, 'matrixList','original')

getInteractions

Description

This method returns a table of interactions of an RNA (interactor) on the RNA of interest.

Usage

getInteractions(cds, interactors)

Arguments

cds

a rnaCrosslinkDataSet object

interactors

A vector containing the names of RNAs to show interactions with

Value

A table showing the read coverage of the specified interacting RNAs

Examples

cds = makeExamplernaCrosslinkDataSet()
getInteractions(cds, c("transcript1","transcript2"))

getMatrices

Description

Make a matrix of contact interactions

Usage

getMatrices(InputList, rna, size)

Arguments

InputList

the original InputList created with readInputFiles or subsetInputList

rna

the RNA of interest that you want to subset

size

The size of the RNA

Details

Function used to create a list of matrices for plotting with plotMatrixList or plotMatrixListFull, the output list will be same as the input except for an extra list layer for the specific RNA

Value

A list of matrices

getReverseInteractions

Description

This method prints interactions of the RNA of interest on another RNA transcript.

Usage

getReverseInteractions(cds, interactor)

Arguments

cds

a rnaCrosslinkDataSet object

interactor

The rna to show interactions with

Value

A long format table shoing the read coverage of chosen RNA

Examples

cds = makeExamplernaCrosslinkDataSet()
getReverseInteractions(cds, 'transcript2')

group

Description

Extract the indeces for each group for the instance

Usage

group(x)

Arguments

x

A rnaCrosslinkDataSet object

Value

A list - The indices of the sample in the control and sample groups

Examples

cds = makeExamplernaCrosslinkDataSet()

group(cds)

makeExamplernaCrosslinkDataSet

Description

Creat a minimal example rnaCrosslinkdataSetObject

Usage

makeExamplernaCrosslinkDataSet()

Value

An example rnaCrosslinkDataSet objext

Examples

cds = makeExamplernaCrosslinkDataSet()

matrixList

Description

Extract the contact matrices

Usage

matrixList(x)

Arguments

x

A rnaCrosslinkDataSet object

Value

A list of contract matrices, one entry for each sample

Examples

cds = makeExamplernaCrosslinkDataSet()

matrixList(cds)

matrixList

Description

Set new matrixList slot

Usage

matrixList(x) <- value

Arguments

x

A rnaCrosslinkDataSet object

value

A replacement

Value

No return - Sets a new matrixList slot

Examples

cds = makeExamplernaCrosslinkDataSet()

newMatrixList <- matrixList(cds)
matrixList(cds) <- newMatrixList

Plot a heatmap that plots the agreements between replicates after clusterrnaCrosslink has been performed

Description

Plot a heatmap that plots the agreements between replicates after clusterrnaCrosslink has been performed

Usage

plotClusterAgreement(cds, analysisStage = "originalClusters")

Arguments

cds

A rnaCrosslinkDataSet object

analysisStage

The stage of the analysis to plot

Value

A heatmap of the agreement between replicates in the analysis stage chosen

Examples


cds = makeExamplernaCrosslinkDataSet()


clusteredCds = clusterrnaCrosslink(cds,
                cores = 1,
                stepCount = 1,
                clusterCutoff = 0)


plotClusterAgreement(cds)

Plot a heatmap that plots the agreements between replicates after clusterrnaCrosslink has been performed

Description

Plot a heatmap that plots the agreements between replicates after clusterrnaCrosslink has been performed

Usage

plotClusterAgreementHeat(cds, analysisStage = "originalClusters")

Arguments

cds

A rnaCrosslinkDataSet object

analysisStage

The stage of the analysis to plot

Value

A heatmap of the agreement between replicates in the analysis stage chosen

Examples


cds = makeExamplernaCrosslinkDataSet()


clusteredCds = clusterrnaCrosslink(cds,
                cores = 1,
                stepCount = 1,
                clusterCutoff = 0)


plotClusterAgreementHeat(cds)

Plots a contact map of two chosen samples for chosen stages in the analysis, with each chosen sample on separate halves of the contact map

Description

Plots a contact map of two chosen samples for chosen stages in the analysis, with each chosen sample on separate halves of the contact map

Usage

plotCombinedMatrix(
  cds,
  type1 = "original",
  type2 = "original",
  sample1 = 1,
  sample2 = 1,
  directory = 0,
  a = 1,
  b = 50,
  c = 1,
  d = 50,
  h = 3,
  returnData = FALSE
)

Arguments

cds

A rnaCrosslinkDataSet object

type1

The analysis stage to plot on the upper half of the heatmap

type2

The analysis stage to plot on the lower half of the heatmap

sample1

The sample number to plot on the upper half of the heatmap

sample2

The sample number to plot on the upper half of the heatmap

directory

An output directory for the heatmap (use 0 for no output)

a

To make a subsetted plot (left value on x)

b

To make a subsetted plot (right value on x)

c

To make a subsetted plot (left value on y)

d

To make a subsetted plot (right value on y)

h

Height of image (inches) (only useful if plotting)

returnData

if TRUE matrix is returned instead of plotting

Value

A heatmap of the reads of the chosen sample numbers, in the analysis stages chosen, with each chosen sample on a separate half of the heatmap

Examples

cds = makeExamplernaCrosslinkDataSet()

plotCombinedMatrix(cds,
            type1 = "original",
            type2 = "noHost",
            b = rnaSize(cds),
            d = rnaSize(cds))

plotComparisonArc

Description

This method plots two structures chosen from the plotEnsemblePCA method

Usage

plotComparisonArc(foldedCds, s1 = "s1", s2 = "s2", n1 = 1, n2 = 2)

Arguments

foldedCds

rnaCrosslinkDataSet after running foldrnaCrosslink

s1

sample of structure 1

s2

sample of structure 2

n1

number of structure from first sample

n2

number of structure from first sample

Value

an ark diagram of the two strcutures

Examples

## Not run: 
cds = makeExamplernaCrosslinkDataSet()
clusteredCds = clusterrnaCrosslink(cds = cds,
                               cores = 3,
                               stepCount = 2,
                               clusterCutoff = 1)


trimmedClusters = trimClusters(clusteredCds = clusteredCds,trimFactor = 1, clusterCutoff = 1)



fasta = paste(c(rep('A',25),
                rep('T',25),
                rep('A',10),
                rep('T',23)),collapse = "")

header = '>transcript1'


fastaFile = tempfile()
writeLines(paste(header,fasta,sep = "\n"),con = fastaFile)


rnaRefs = list()
rnaRefs[[rnas(cds)]] = read.fasta(fastaFile)
rnaRefs



foldedCds = foldrnaCrosslink(trimmedClusters,
                         rnaRefs = rnaRefs,
                         start = 1,
                         end = 83,
                         shape = 0,
                         ensembl = 5,
                         constraintNumber  = 1,
                         evCutoff = 1)


plotComparisonArc(foldedCds,"s1","s1",1,3)

## End(Not run)

plotEnsemblePCA

Description

This method plots a PCA of the ensembl

Usage

plotEnsemblePCA(foldedCds, labels = TRUE, split = TRUE)

Arguments

foldedCds

rnaCrosslinkDataSet after running foldrnaCrosslink

labels

plot with labels or not (TRUE/FALSE)

split

split the plot using facets based on the samples (TRUE/FALSE)

Value

a PCA plot of the ensembl

Examples

## Not run: 
cds = makeExamplernaCrosslinkDataSet()
clusteredCds = clusterrnaCrosslink(cds = cds,
                               cores = 3,
                               stepCount = 2,
                               clusterCutoff = 1)


trimmedClusters = trimClusters(clusteredCds = clusteredCds,trimFactor = 1, clusterCutoff = 1)



fasta = paste(c(rep('A',25),
                rep('T',25),
                rep('A',10),
                rep('T',23)),collapse = "")

header = '>transcript1'


fastaFile = tempfile()
writeLines(paste(header,fasta,sep = "\n"),con = fastaFile)


rnaRefs = list()
rnaRefs[[rnas(cds)]] = read.fasta(fastaFile)
rnaRefs



foldedCds = foldrnaCrosslink(trimmedClusters,
                         rnaRefs = rnaRefs,
                         start = 1,
                         end = 83,
                         shape = 0,
                         ensembl = 5,
                         constraintNumber  = 1,
                         evCutoff = 1)


plotEnsemblePCA(foldedCds)

## End(Not run)

Plots a contact map of interactions of each sample of an RNA (interactor) on the RNA of interest

Description

Plots a contact map of interactions of each sample of an RNA (interactor) on the RNA of interest

Usage

plotInteractions(
  cds,
  rna,
  interactor,
  directory = 0,
  a = 1,
  b = 50,
  c = 1,
  d = 50,
  h = 3
)

Arguments

cds

A rnaCrosslinkDataSet object

rna

The RNA of interest

interactor

The RNA to show interactions with

directory

An output directory for the heatmap (use 0 for no output)

a

To make a subsetted plot (left value on x)

b

To make a subsetted plot (right value on x) (use 'max' to plot the whole RNA strand length)

c

To make a subsetted plot (left value on y)

d

To make a subsetted plot (right value on y) (use 'max' to plot the whole RNA strand length)

h

Height of image (inches) (only useful if plotting)

Value

A heatmap of interactions of the RNA (interactor) on the RNA of interest

Examples

cds = makeExamplernaCrosslinkDataSet()

plotInteractions(cds,
            rna = "transcript1",
            interactor = "transcript2",
            b = "max",
            d = "max")

Plots a contact map of interactions of all samples of an RNA (interactor) on the RNA of interest

Description

Plots a contact map of interactions of all samples of an RNA (interactor) on the RNA of interest

Usage

plotInteractionsAverage(
  cds,
  rna,
  interactor,
  directory = 0,
  a = 1,
  b = 50,
  c = 1,
  d = 50,
  h = 3
)

Arguments

cds

A rnaCrosslinkDataSet object

rna

The RNA of interest

interactor

The RNA to show interactions with

directory

An output directory for the heatmap (use 0 for no output)

a

To make a subsetted plot (left value on x)

b

To make a subsetted plot (right value on x) (use 'max' to plot the whole RNA strand length)

c

To make a subsetted plot (left value on y)

d

To make a subsetted plot (right value on y) (use 'max' to plot the whole RNA strand length)

h

Height of image (inches) (only useful if plotting)

Value

A heatmap of interactions of all samples of the RNA (interactor) on the RNA of interest

Examples

cds = makeExamplernaCrosslinkDataSet()

plotInteractionsAverage(cds,
            rna = "transcript1",
            interactor = "transcript2",
            b = "max",
            d = "max")

Plots a number of contact maps to file of each sample for a stage in the analysis

Description

Plots a number of contact maps to file of each sample for a stage in the analysis

Usage

plotMatrices(
  cds,
  type = "original",
  directory = 0,
  a = 1,
  b = 50,
  c = 1,
  d = 50,
  h = 3
)

Arguments

cds

A rnaCrosslinkDataSet object

type

The analysis stage to plot

directory

An output directory for the heatmap (use 0 for no output)

a

To make a subsetted plot (left value on x)

b

To make a subsetted plot (right value on x)

c

To make a subsetted plot (left value on y)

d

To make a subsetted plot (right value on y)

h

Height of image (inches) ( only useful if plotting)

Value

A heatmap of the reads in the analysis stage chosen

Examples

cds = makeExamplernaCrosslinkDataSet()

plotMatrices(cds,
            b = rnaSize(cds),
            d = rnaSize(cds))

plotMatricesAverage

Description

Plots a contact map of all samples for two chosen stages in the analysis, with each chosen stage on separate halves of the contact map

Usage

plotMatricesAverage(
  cds,
  type1 = "original",
  type2 = "blank",
  directory = 0,
  a = 1,
  b = 50,
  c = 1,
  d = 50,
  h = 3
)

Arguments

cds

A rnaCrosslinkDataSet object

type1

The analysis stage to plot on the upper half of the heatmap (use 'blank' to leave this half blank)

type2

The analysis stage to plot on the lower half of the heatmap (use 'blank' to leave this half blank)

directory

An output directory for the heatmap (use 0 for no output)

a

To make a subsetted plot (left value on x)

b

To make a subsetted plot (right value on x)

c

To make a subsetted plot (left value on y)

d

To make a subsetted plot (right value on y)

h

Height of image (inches) ( only useful if plotting)

Value

A heatmap of the reads in the two analysis stages chosen, with each chosen stage on a separate half of the heatmap

Examples

cds = makeExamplernaCrosslinkDataSet()

plotMatricesAverage(cds,
            b = rnaSize(cds),
            d = rnaSize(cds))

plotStructure

Description

This method plots a structures chosen from the plotEnsemblePCA method

Usage

plotStructure(foldedCds, rnaRefs, s = "s1", n = 1)

Arguments

foldedCds

rnaCrosslinkDataSet after running foldrnaCrosslink

rnaRefs

A fasta of the transcript (made with seqinr::read.fasta)

s

sample of structure

n

number of structure

Value

a diagram of the predicted structure

Examples

## Not run: 
cds = makeExamplernaCrosslinkDataSet()
clusteredCds = clusterrnaCrosslink(cds = cds,
                               cores = 3,
                               stepCount = 2,
                               clusterCutoff = 1)


trimmedClusters = trimClusters(clusteredCds = clusteredCds,trimFactor = 1, clusterCutoff = 1)



fasta = paste(c(rep('A',25),
                rep('T',25),
                rep('A',10),
                rep('T',23)),collapse = "")

header = '>transcript1'


fastaFile = tempfile()
writeLines(paste(header,fasta,sep = "\n"),con = fastaFile)


rnaRefs = list()
rnaRefs[[rnas(cds)]] = read.fasta(fastaFile)
rnaRefs



foldedCds = foldrnaCrosslink(trimmedClusters,
                         rnaRefs = rnaRefs,
                         start = 1,
                         end = 83,
                         shape = 0,
                         ensembl = 5,
                         constraintNumber  = 1,
                         evCutoff = 1)


plotStructure(foldedCds,rnaRefs,"s1",3)

## End(Not run)

printClustersFast

Description

Makes a table with the coordinates of the clusters

Usage

printClustersFast(dir, clustering, highest_clusters, left, right)

Arguments

dir

the directory that contains the *Inputrids.Input files

clustering

The output from the iGraph function cluster_walktrap for the (made with adjacency matrix input)

highest_clusters

The cluster you are interested in keeping

left

list created with InputToGRanges (but just the left section of the list)

right

list created with InputToGRanges (but just the right section of the list)

Details

Does the same as printClusters but is a lot faster and does not create plots of each cluster

Value

A table of clusters and coordinates

readNumbers

Description

This method prints a table showing the number of duplexes in the clusters in each step of the analysis

Usage

readNumbers(knowClusteredCds, rna)

Arguments

knowClusteredCds

A rnaCrosslinkDataSet object after clustering has been performed

rna

The RNA ID of interest - use rna(cdsObject).

Value

A data.frame shoing the number of reads in clusters for each sample

Examples

cds = makeExamplernaCrosslinkDataSet()

clusteredCds = clusterrnaCrosslink(cds,
                cores = 1,
                stepCount = 1,
                clusterCutoff = 1)
readNumbers(clusteredCds)

rnaCrosslinkDataSet

Description

rnaCrosslinkDataSet objects are used to store the input meta-data, data and create a framework for the storage of results. Whilst creating the object, the original Input files are also filtered for the RNA of interest. Check the package vignette for more information.

Usage

rnaCrosslinkDataSet(
  rnas,
  rnaSize = 0,
  sampleTable,
  subset = "all",
  sample = "all"
)

Arguments

rnas

vector - The names of the RNA interest, these must be displayed the same way as in the input Input Files.

rnaSize

named list - The sizes (nt) of the RNAs of interest, the list elements must have same names as the rnas vector and each each contain one numeric value.

sampleTable

string - The address of the sample table, the sample table must have 4 columns, fileName (the full path and file name of the input Input file for each sample ), group ("s" - sample or "c" - control), sample (1,2,3, etc), sampleName (must be unique).

subset

a vector of 4 values to subset based on structural read size. c(l-min,l-max,r-min,r-max)

sample

The number of reads to sample for each sample.

Value

A rnaCrosslinkDataSet object.

Slots

clusterTableFolded

table - a table similar to the clusterTableList it contains coordinates of the clusters along with vienna format fold and RNA sequences for each cluster

clusterTableList

List - Follows the pattern for list slots of rnaCrosslinkDataSet objects, matrixList(cds)[[rna]][[type]][[sample]]. contains a table with coordinates and information about the clusters identified

clusterGrangesList

List - Follows the pattern for list slots of rnaCrosslinkDataSet objects, matrixList(cds)[[rna]][[type]][[sample]]. contains GRanges objects of the original duplexes with their cluster membership

sampleTable

table - Column names; fileName, group (s or c), sample (1,2,3, etc), sampleName (must be unique)

rnas

string - a single RNA to analyse - must be present in rnas(cdsObject)

rnaSize

if set to 0 this will be calculated

matrixList

List - Follows the pattern for list slots of rnaCrosslinkDataSet objects, matrixList(cds)[[rna]][[type]][[sample]]. Contains a set of contact matrices, each cell contains the number of duplexes identified for position x,y.

InputFiles

List - Follows the pattern for list slots of rnaCrosslinkDataSet objects, InputFiles(cds)[[rna]][[type]][[sample]]. Contains a set of tables, these are the original Input files that were read in.

interactionTable

Table of interactions discovered in step1 of the folding

viennaStructures

List of vienna format structures from final prediction

dgs

List of free energies

Examples

# make example input
cds = makeExamplernaCrosslinkDataSet()

cds

rnaCrosslinkOO: A package for analysing a rnaCrosslink dataset

Description

The package consists of 1 Object: rnaCrosslinkDataSet

The package consists of 1 Object: rnaCrosslinkDataSet

rnaCrosslinkDataSet

NA

NA

Author(s)

Maintainer: Jonathan Price jlp76@cam.ac.uk (ORCID)

Other contributors:

See Also

Useful links:

Useful links:

rnaCrosslinkQC

Description

get a plot fo the read lengths and transcripts in the dataset The fucntion will make 1 pdf and 2 text file in the directory provided

Usage

rnaCrosslinkQC(sampleTable, directory, topTranscripts = TRUE)

Arguments

sampleTable

string - The address of the sample table, the sample table must have 4 columns, fileName (the full path and file name of the input Input file for each sample ), group ("s" - sample or "c" - control), sample (1,2,3, etc), sampleName (must be unique).

directory

A directory address to write the files

topTranscripts

If FALSE a table of top trandscirpts will not be written to file

Value

ggplot and txt file

Examples

  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 = FALSE)
 
 
 
 
 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 = FALSE)

 
 # 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")

rnaCrosslinkQC(sampleTable2,tempdir())

rnaSize

Description

Extract the size of the RNA for the instance

Usage

rnaSize(x)

Arguments

x

A rnaCrosslinkDataSet object

Value

A numeric - the size of the RNA (nucleotides)

Examples

cds = makeExamplernaCrosslinkDataSet()

rnaSize(cds)

rnas

Description

Extract the rna ID for the instance

Usage

rnas(x)

Arguments

x

A rnaCrosslinkDataSet object

Value

A character - the ID of the RNA

Examples

cds = makeExamplernaCrosslinkDataSet()

rnas(cds)

sampleChimeras

Description

This function samples chimeras into smaller chunks so that clustering is quicker

Usage

sampleChimeras(chimeraList)

Arguments

chimeraList

list of chimeras

sampleNames

Description

Extract the sample names for the instance

Usage

sampleNames(x)

Arguments

x

A rnaCrosslinkDataSet object

Value

A character vector - the sample names

Examples

cds = makeExamplernaCrosslinkDataSet()

sampleNames(cds)

sampleTable

Description

Extract the sample table for the instance

Usage

sampleTable(x)

Arguments

x

A rnaCrosslinkDataSet object

Value

A data frame - The orginal meta-data table

Examples

cds = makeExamplernaCrosslinkDataSet()

sampleTable(cds)

subsetInputList2

Description

Subset a list of Input files

Usage

subsetInputList2(InputList, min, max, length)

Arguments

InputList

the original InputList created with readInputFiles

min

the rna of interest that you want to subset

max

The number of randomly subsetted chimeric reads you need

length

The number of randomly subsetted chimeric reads you need

Details

Function used to subset a list of Input data created by readInputFiles This function produces the same size list as before but it returns ONLY the rna of interest and also Choose duplexes where the nt difference in position between the one side and other side of an interaction is between min and max

Value

A list of subsetted Input files

swapInputs

Description

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.

Usage

swapInputs(InputList, rna)

Arguments

InputList

the original InputList created with readInputFiles or subsetInputList

rna

The rna of interest

Value

A list of "swapped" Input datas

swapInputs2

Description

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.

Usage

swapInputs2(InputList, rna)

Arguments

InputList

the original InputList created with readInputFiles or subsetInputList

rna

The rna of interest

Value

A list of "swapped" Input data

swapInputs3

Description

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.

Usage

swapInputs3(InputList, rna)

Arguments

InputList

the original InputList created with readInputFiles or subsetInputList

rna

The rna of interest

Value

A list of "swapped" Input datas

topInteracters

Description

This method prints the top transcripts that have the most duplexes assigned that interact with the transcript of interest

Usage

topInteracters(cds, ntop = 10, sds = TRUE)

Arguments

cds

a rnaCrosslinkDataSet object

ntop

the number of entries to display

sds

known bug, doesn't work for small data sets fix incoming

Value

A table, the number of counts per sample per interacting transcript

Examples

cds = makeExamplernaCrosslinkDataSet()
topInteracters(cds, sds = TRUE)

topInteractions

Description

This method prints the top transcript interactions that have the most duplexes assigned

Usage

topInteractions(cds, ntop = 10)

Arguments

cds

a rnaCrosslinkDataSet object

ntop

the number of entries to display

Value

A table, the number of counts per sample per interaction

Examples

cds = makeExamplernaCrosslinkDataSet()
topInteractions(cds)

topTranscripts

Description

This method prints the top transcripts that have the most duplexes assigned

Usage

topTranscripts(cds, ntop = 10)

Arguments

cds

a rnaCrosslinkDataSet object

ntop

the number of entries to display

Value

A table, the number of counts per sample per transcript

Examples

cds = makeExamplernaCrosslinkDataSet()
topTranscripts(cds)

trimClusters

Description

Trimming of the clusters removes redundant information derived from random fragmentation of the reads during library preparation. This method takes a rnaCrosslinkDataSet object where clustering has been performed with the clusterrnaCrosslink method and trims the clusters according to the trimFactor argument.

Usage

trimClusters(clusteredCds, trimFactor = 2.5, clusterCutoff = 1)

Arguments

clusteredCds

a rnaCrosslinkDataSet object

trimFactor

a positive value that defines how much the clusters will

clusterCutoff

Minimum number of reads before discarding cluster be trimmed = mean + ( sd * trimFactor )

Details

The 3 attributes; matrixList, clusterTableList and clusterGrangesList will gain the types "superClusters" and "trimmedClusters"

Value

Returns a rnaCrosslinkDataSet object

Examples

cds = makeExamplernaCrosslinkDataSet()

clusteredCds = clusterrnaCrosslink(cds,
                cores = 1,
                stepCount = 1,
                clusterCutoff = 0)
                
trimClusters(clusteredCds = clusteredCds,
             trimFactor = 1, 
             clusterCutoff = 0)