Name Mode Size
R 040000
data 040000
inst 040000
man 040000
src 040000
tests 040000
vignettes 040000
.Rbuildignore 100755 0 kb
.travis.yml 100755 0 kb
DESCRIPTION 100644 3 kb
LICENSE 100644 9 kb
NAMESPACE 100755 7 kb 100755 4 kb
<!-- badges: start --> ![Bioconductor build]( ![Bioconductor platforms]( ![Bioconductor dependencies]( </br> ![GitHub]( ![GitHub repo size]( ![GitHub issues]( <!-- badges: end --> # metaseqR2 An R package for the analysis, meta-analysis and result reporting of RNA-Seq gene expression data - Next Generation! ## Citation metaseqR2 along with further research regarding the abilities of the PANDORA algorithm was published in [Briefings in Bioinformatics]( If you use metaseqR2 in your research, please cite: > Dionysios Fanidis, Panagiotis Moulos: **Integrative, normalization-insusceptible statistical analysis of RNA-Seq data, with improved differential expression and unbiased downstream functional analysis**, *Briefings in Bioinformatics*, 2020, bbaa156, DOI: [10.1093/bib/bbaa156]( ## Installation from Bioconductor ``` if (!requireNamespace("BiocManager",quietly=TRUE)) install.packages("BiocManager") library(BiocManager) BiocManager::install("metaseqR2") # or for development version to be installed # BiocManager::install("metaseqR2",version="devel") ``` ## Installation from GitHub Use with caution as the latest version may be unstable, although typical Bioconductor checks are executed before each push. ``` if (!requireNamespace("devtools",quietly=TRUE)) install.packages("devtools") library(devtools) install_github("pmoulos/metaseqR2") ``` ## Installation from source The same things apply regarding stability. ``` git clone mkdir metaseqR2-build rsync -avr --exclude=.git --exclude=.gitignore \ ./metaseqR2-local/ ./metaseqR2-build/metaseqR2 cd ./metaseqR2-build R CMD build ./metaseqR2 ``` This will take some time to build the vignettes. If you do not need them: ``` R CMD build --no-build-vignettes ./metaseqR2 ``` And then install ``` R CMD INSTALL ./metaseqR2_x.y.z.tar.gz ``` Please report any issues [here]( ## metaseqR2 annotation database If you do not wish to build annotation databases on your own using the ```buildAnnotationDatabase``` function, you can find complete pre-built annotation SQLite databases [here]( New versions will be constructed from time to time, most probably whenever a new Ensembl release comes live. The prebuilt annotations contain: * Annotations for all supported organsisms for all types of metaseqR2 analyses. * For every supported organism: + For the latest version of each genome, the latest two Ensembl required annotations. + For all other versions of each genome, the latest Ensembl required annotations supporting that particular version. + UCSC and RefSeq annotations as fetched in the day of the build (denoted by the folder name in the above link). The SQLite database must be placed in ```system.file(package="metaseqR2")``` and named ```annotation.sqlite```, that is ```file.path(system.file(package="metaseqR2"),"annotation.sqlite")```. Otherwise you will have to provide your desired location in each ```metaseqr2``` call. Alternatively, on-the-fly download is still supported but is inneficient. ## List of required packages metaseqR2 would benefit from the existence of all the following packages: * ABSSeq * Biobase * BiocGenerics * BiocManager * BiocParallel * BiocStyle * biomaRt * Biostrings * BSgenome * corrplot * DESeq2 * DSS * DT * EDASeq * edgeR * harmonicmeanp * genefilter * GenomeInfoDb * GenomicAlignments * GenomicFeatures * GenomicRanges * gplots * graphics * grDevices * heatmaply * htmltools * httr * IRanges * jsonlite * knitr * limma * log4r * magrittr * Matrix * methods * NBPSeq * pander * parallel * qvalue * rmarkdown * rmdformats * RMySQL * Rsamtools * RSQLite * rtracklayer * RUnit * S4Vectors * splines * stats * stringr * SummarizedExperiment * survcomp * TCC * utils * VennDiagram * vsn * zoo A recent version of [Pandoc]( is also required, ideally above 2.0.