README.md

# High-Throughput RNA-seq model fit

![pipeline running](https://gitbio.ens-lyon.fr/LBMC/yvertlab/vortex/plasticity_mutation/HTRfit/badges/master/pipeline.svg)
![Latest release](https://gitbio.ens-lyon.fr/LBMC/yvertlab/vortex/plasticity_mutation/HTRfit/-/badges/release.svg)
![Licence](https://img.shields.io/badge/License-GPLv3-blue.svg)
![coverage](https://gitbio.ens-lyon.fr/LBMC/yvertlab/vortex/plasticity_mutation/HTRfit/badges/master/coverage.svg)

## 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:

* [ Theory behind HTRfit ](https://htrfit-lbmc-yvertlab-vortex-plasticity-mutation-477701eb488dfd9.gitbiopages.ens-lyon.fr/articles/01-theoryBehindHtrfit.html)
* [ Simulation tutorial ](https://htrfit-lbmc-yvertlab-vortex-plasticity-mutation-477701eb488dfd9.gitbiopages.ens-lyon.fr/articles/02-tutorial.html) 
* [ RNAseq analysis with HTRfit ](https://htrfit-lbmc-yvertlab-vortex-plasticity-mutation-477701eb488dfd9.gitbiopages.ens-lyon.fr/articles/03-rnaseq_analysis.html)
* [ Benchmarking HTRfit/DESeq2 ](https://htrfit-lbmc-yvertlab-vortex-plasticity-mutation-477701eb488dfd9.gitbiopages.ens-lyon.fr/articles/04-htrfit_vs_deseq2.html)


## Installation

#### method A:  

To install the latest version of HTRfit, run the following in your R console :
```r
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](https://gitbio.ens-lyon.fr/aduvermy/HTRfit/-/tags). Once you've downloaded the release, simply launch following command.

```r
## -- 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:

```r
## -- 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](https://hub.docker.com/r/ruanad/htrfit/tags) to simplify the package's utilization.

```sh
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](https://biosphere.france-bioinformatique.fr/catalogue/). 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](#method-a).