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grandR

Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA seq. Such experiments require specialized tools for primary processing such as GRAND-SLAM, and specialized tools for downstream analyses. grandR provides a comprehensive toolbox for quality control, kinetic modeling, differential gene expression analysis and visualization of such data.

Installation

grandR is available from CRAN. Install grandR using the following commands on the R console:

install.packages("grandR")
library(grandR)

You can also install the development version from github:

require("devtools")
devtools::install_github("erhard-lab/grandR")
library(grandR)

System Requirements

grandR should be compatible with Windows, Mac, and Linux operating systems, but we recommend using grandR on a Linux machine, where it has been extensively tested (Ubuntu 22.04). Due to restrictions of the parallel package, parallelization (SetParallel()) does not work under Windows. grandR runs on standard laptops (multi-core CPUs are recommended and memory requirements depend on the size of your data sets).

Installing it via install.packages or devtools::install_github will make sure that the following (standard) packages are available:

stats,Matrix,rlang,ggplot2,grDevices,patchwork,RCurl,plyr,parallel,reshape2,MASS,scales,cowplot,minpack.lm,lfc,labeling,methods,utils,numDeriv

Additional packages are optional and important for particular functions:

knitr, rmarkdown, circlize, Seurat, ComplexHeatmap, ggrepel, DESeq2, S4Vectors, data.table, clusterProfiler, biomaRt, msigdbr, fgsea, rclipboard, cubature, DT, RColorBrewer, gsl, htmltools, matrixStats, monocle, VGAM, quantreg, graphics, shiny, ggrastr, viridisLite

With all dependencies available, installation of grandR typically takes less than a minute.

Cheatsheet

How to get started

First have a look at the Getting started vignette.

Then, go through the Differential expression or the Kinetic modeling vignette, which provide a comprehensive walk-through of the two main settings of nucleotide conversion experiments.

There are also additional vignettes:

• Loading data and working with grandR objects: Learn more about programming with grandR
• Working with data matrices and analysis results: Learn more about how to retrieve data from grandR objects
• Plotting: Learn about the plotting helper functions and the shiny web-interface of grandR
• Pulse-chase: Learn how to fit pulse-chase data with grandR
• Single cell: Learn how to load and process single cell data with grandR
• 4sU dropout: Learn how to perform analysis of 4sU dropout

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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