Skip to content
Snippets Groups Projects
aduvermy's avatar
Arnaud Duvermy authored
Former-commit-id: 2399c691da97853016d58199537d5fcfa2a01abc
Former-commit-id: fc8f2aabe57e467786f58ea148410a5ccc961a71
Former-commit-id: d0fa8a56710786744eb88353cf5fe0624b2cefce
37c29f73
History

High-Throughput RNA-seq model fit

pipeline running Latest release Licence coverage

Why use HTRfit

HTRfit provides a robust statistical framework that allows you to investigate the essential experimental parameters influencing your ability to detect expression changes. Whether you're examining sequencing depth, the number of replicates, or other critical factors, HTRfit's computational simulation is your go-to solution.

Furthermore, by enabling the inclusion of fixed effects, mixed effects, and interactions in your RNAseq data analysis, HTRfit provides the flexibility needed to conduct your differential expression analysis effectively. HTRfit is particularly adapted for the analysis of large number of samples, or highly multiplexed experiments.

Getting started

Our documentation includes a few example applications showing how to use our package:

Installation

method A:

To install the latest version of HTRfit, run the following in your R console :

if (!requireNamespace("remotes", quietly = TRUE))
    install.packages("remotes")
remotes::install_git("https://gitbio.ens-lyon.fr/aduvermy/HTRfit")

method B:

You also have the option to download a release directly from the HTRfit release page. Once you've downloaded the release, simply launch following command.

## -- Example using the HTRfit-v2.0.0 release
install.packages('HTRfit-v2.0.0.tar.gz', repos = NULL, type='source')

When dependencies are met, installation should take a few minutes.

CRAN packages dependencies

The following depandencies are required:

## -- required
install.packages(c('parallel', 'data.table', 'ggplot2', 'gridExtra', 
                    'glmmTMB', 'magrittr', 'MASS', 'reshape2', 
                    'rlang', 'stats', 'utils', 'BiocManager', 'car'))
BiocManager::install('S4Vectors', update = FALSE)
## -- optional 
BiocManager::install('DESeq2', update = FALSE)

Docker

We have developed Docker images to simplify the package's utilization.

docker pull ruanad/htrfit:v2.0.0
docker run -it --rm ruanad/htrfit:v2.0.0

Biosphere virtual machine

A straightforward way to use HTRfit is to run it on a Virtual Machine (VM) through Biosphere. We recommend utilizing a VM that includes RStudio for an integrated development environment (IDE) experience. Biosphere VM resources can also be scaled according to your simulation needs.
HTRfit can be installed using the method A.