easyRNASeq package {easyRNASeq} | R Documentation |
Offers functionalities to summarize read counts per feature of interest, e.g. exons, transcripts, genes, etc.
Offers functionalities to normalize the summarized counts using 3rd party packages like DESeq
or edgeR
.
The main function easyRNASeq
will summarize the counts per
feature of interest, for as many samples as provided and will return a
count matrix (N*M) where N are the features and M the samples.
This data can be corrected to RPKM in which case
a matrix of corrected value is returned instead, with the same dimensions.
Alternatively a RangedSummarizedExperiment
can be returned and this
is expected to be the default in the upcoming version of easyRNASeq (as of 1.5.x).
If the necessary sample
information are provided, the data can be normalized using either DESeq
or edgeR
and the corresponding package object returned.
For more insider details, and step by step functions, see:
ShortRead methods for pre-processing the data.
easyRNASeq annotation methods for getting the annotation.
easyRNASeq coverage methods for computing the coverage from a Short Read Alignment file.
easyRNASeq summarization methods for summarizing the data.
easyRNASeq correction methods for correcting the data (i.e. generating RPKM).
edgeR methods for post-processing the data.
DESeq methods for post-processing the data.
|
Nicolas Delhomme, Bastian Schiffthaler, Ismael Padioleau
The class RNAseq specification:
RNAseq
The default output class specification:
RangedSummarizedExperiment
The imported packages:
biomaRt
BiocParallel
edgeR
genomeIntervals
Biostrings
BSgenome
DESeq
GenomicRanges
IRanges
Rsamtools
ShortRead
The suggested packages:
parallel
GenomicFeatures
The following classes and functions that are made available from other packages:
Classes
BamFileList
CountDataSet
RangedData
RangedSummarizedExperiment
Functions/Methods
DESeq estimate size factor and
estimate dispersion functions
The RangedSummarizedExperiment assay accessor
The locfit function
The BamFileList constructor
The IRanges constructor
The RangedData constructor
For the SRFilterResult,
chromosomeFilter, compose and nFilter methods
# get the example annotation file - we retrieve a gtf file from GitHub library(curl) invisible(curl_download(paste0("https://github.com/UPSCb/UPSCb/raw/", "master/tutorial/easyRNASeq/Drosophila_melanogaster.BDGP5.77.with-chr.gtf.gz"), "Drosophila_melanogaster.BDGP5.77.with-chr.gtf.gz")) # get the example data files - we retrieve a set of example bam files # from GitHub using curl, as well as their index. invisible(sapply(c("ACACTG","ACTAGC"),function(bam){ curl_download(paste0("https://github.com/UPSCb/UPSCb/raw/", "master/tutorial/easyRNASeq/",bam,".bam"),paste0(bam,".bam")) curl_download(paste0("https://github.com/UPSCb/UPSCb/raw/", "master/tutorial/easyRNASeq/",bam,".bam.bai"),paste0(bam,".bam.bai")) })) # create the AnnotParam annotParam <- AnnotParam( datasource="Drosophila_melanogaster.BDGP5.77.with-chr.gtf.gz", type="gtf") # create the synthetic transcripts annotParam <- createSyntheticTranscripts(annotParam,verbose=FALSE) # create the RnaSeqParam rnaSeqParam <- RnaSeqParam(annotParam=annotParam,countBy="gene") # get the bamfiles bamFiles <- getBamFileList(dir(pattern="^[A,T].*\\.bam$",full.names=TRUE)) # get a RangedSummarizedExperiment containing the counts table sexp <- simpleRNASeq( bamFiles=bamFiles, param=rnaSeqParam, verbose=TRUE ) # get the counts assays(sexp)$genes