PileupSequenceData

The PileupSequenceData aggregates the pileup of called bases per position.

PileupSequenceData contains five columns per data file named using the following naming convention pileup.condition.replicate. The five columns are distinguished by additional identifiers -, G, A, T and C.

aggregate calculates the mean and sd for each nucleotide in the control and treated condition separatly. The results are then normalized to a row sum of 1.

PileupSequenceDataFrame(
  df,
  ranges,
  sequence,
  replicate,
  condition,
  bamfiles,
  seqinfo
)

PileupSequenceData(bamfiles, annotation, sequences, seqinfo, ...)

# S4 method for PileupSequenceData,BamFileList,GRangesList,XStringSet,ScanBamParam
getData(x, bamfiles, grl, sequences, param, args)

# S4 method for PileupSequenceData
aggregateData(x, condition = c("Both", "Treated", "Control"))

# S4 method for PileupSequenceData
getDataTrack(x, name, ...)

pileupToCoverage(x)

# S4 method for PileupSequenceData
pileupToCoverage(x)

Arguments

df, ranges, sequence, replicate

inputs for creating a SequenceDataFrame. See SequenceDataFrame.

condition

For aggregate: condition for which the data should be aggregated.

bamfiles, annotation, seqinfo, grl, sequences, param, args, ...

See SequenceData and SequenceData-functions

x

a PileupSequenceData

name

For getDataTrack: a valid transcript name. Must be a name of ranges(x)

Value

a PileupSequenceData object

Examples

# Construction of a PileupSequenceData object
library(RNAmodR.Data)
library(rtracklayer)
annotation <- GFF3File(RNAmodR.Data.example.man.gff3())
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
sequences <- RNAmodR.Data.example.man.fasta()
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
files <- c(treated = RNAmodR.Data.example.wt.1())
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
psd <- PileupSequenceData(files, annotation = annotation,
                          sequences = sequences)
#> Import genomic features from the file as a GRanges object ... 
#> OK
#> Prepare the 'metadata' data frame ... 
#> OK
#> Make the TxDb object ... 
#> OK
#> Loading Pileup data from BAM files ... 
#> OK