R/AllGenerics.R, R/SequenceData-stats.R
stats.Rdstats returns information about reads used in the RNAmodR analysis.
Three modes are available depending on which type of object is provided. If a
SequenceData object is provided, a
BamFile or
BamFileList must be provided as well. If a
Modifier object is used, the bam files
returned from the bamfiles function are used. This is also the case,
if a ModifierSet object is used.
stats(x, file, ...)
# S4 method for SequenceData,BamFile
stats(x, file, ...)
# S4 method for SequenceData,BamFileList
stats(x, file, ...)
# S4 method for Modifier,missing
stats(x)
# S4 method for ModifierSet,missing
stats(x)a SequenceData,
Modifier or
ModifierSet object
a BamFile or
BamFileList, if x is a
SequenceData object.
optional parameters used as stated
here (except minQuality),
if x is a SequenceData object.
a DataFrame, DataFrameList or SimpleList with
the results in aggregated form
library(RNAmodR.Data)
library(rtracklayer)
sequences <- RNAmodR.Data.example.AAS.fasta()
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
annotation <- GFF3File(RNAmodR.Data.example.AAS.gff3())
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
files <- list("SampleSet1" = c(treated = RNAmodR.Data.example.wt.1(),
treated = RNAmodR.Data.example.wt.2(),
treated = RNAmodR.Data.example.wt.3()),
"SampleSet2" = c(treated = RNAmodR.Data.example.bud23.1(),
treated = RNAmodR.Data.example.bud23.2()),
"SampleSet3" = c(treated = RNAmodR.Data.example.trm8.1(),
treated = RNAmodR.Data.example.trm8.2()))
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
#> see ?RNAmodR.Data and browseVignettes('RNAmodR.Data') for documentation
#> loading from cache
msi <- ModSetInosine(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
# smallest chunk of information
stats(sequenceData(msi[[1L]]),bamfiles(msi[[1L]])[[1L]])
#> DataFrame with 12 rows and 6 columns
#> seqnames seqlength mapped unmapped used used_distro
#> <factor> <integer> <numeric> <numeric> <IntegerList> <List>
#> 1 chr1 1800 197050 0 159782 83,1252,860,...
#> 2 chr2 85 5863 0 2459 2,16,16,...
#> 3 chr3 76 76905 0 63497 35,478,4106,...
#> 4 chr4 77 8299 0 6554 6,27,36,...
#> 5 chr5 74 11758 0 8818 520,105,93,...
#> ... ... ... ... ... ... ...
#> 8 chr8 75 144293 0 143068 14,44,48,...
#> 9 chr9 75 13790 0 9753 1,49,43,...
#> 10 chr10 85 19861 0 17729 35,21,10,...
#> 11 chr11 77 10532 0 9086 53,92,185,...
#> 12 * 0 0 961095 NA NA
# partial information
stats(sequenceData(msi[[1L]]),bamfiles(msi[[1L]]))
#> DataFrameList of length 3
#> names(3): treated treated treated
# the whole stats
stats(msi)
#> List of length 3
#> names(3): SampleSet1 SampleSet2 SampleSet3