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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup, include=FALSE}
library(rRMSAnalyzer)
```
# rRMSAnalyzer: package to analyze RiboMethSeq data
RiboMethSeq is an RNAseq-based approach to analyze 2'O-ribose methylation (2'Ome).
rRMSAnalyzer is an R package that provides a set of easy-to-use functions to evaluate 2'Ome levels by computing C-scores from RiboMethSeq read end counts as input.
Available features (version 2):
- C-score computation (using either mean or median for the window of neighboring positions)
- Batch effect adjustment with CombatSeq
- Different visualizations to compare samples or sites
- Include a table of annotated human rRNA sites
- Export computed C-scores into a dataframe
- Semi-automated quality control report
> **Note** We have also developed a [dedicated Nextflow pipeline](https://github.com/RibosomeCRCL/ribomethseq-nf) to process the data from sequencing output (fastq files) to useful raw data for rRMSAnalyzer (read end counts).
## Installation
The latest version of rRMSAnalyzer package can be installed from Github with:
```{r, eval=FALSE}
library(devtools)
devtools::install_github("RibosomeCRCL/rRMSAnalyzer")
```
## Usage
```{r, eval = FALSE}
library(rRMSAnalyzer)
ribo <- load_ribodata(
count_path = "/path/to/your/csvfiles/directory/",
metadata = "path/to/metadata.csv",
metadata_key = "filename",
metadata_id = "samplename")
# Compute the c-score using different parameters,
# including calculation of the local coverage using the mean instead of the median
ribo <- compute_cscore(ribo, method = "mean")
# If necessary, adjust any technical biases using ComBat-Seq.
# Here, as an example, we use the "library" column in metadata.
ribo <- adjust_bias(ribo,"library")
# Plot a Principal Component Analysis (PCA) whose colors depend on the "condition" column in metadata
plot_pca(ribo,"condition")
```
## Getting started
The "getting started" is available on our website: https://ribosomecrcl.github.io/rRMSAnalyzer/
A test dataset (ribo_toy) is included in the package.
## Help, bug reports and suggestions
To report a bug or any suggestion to improve the package, please let us known by opening a new issue on: <https://github.com/RibosomeCRCL/rRMSAnalyzer/issues>
## Acknowledgements
We would like to thank all our collaborators from Jean-Jacques Diaz Team and the Bioinformatic Platform Gilles Thomas for their advices and suggestions.
## Funding
This project has been funded by the French Cancer Institute (INCa, PLBIO 2019-138 MARACAS), the SIRIC Program (INCa-DGOS-Inserm_12563 LyRICAN), LabEX program (DEVweCan), the French association Ligue Nationale Contre le Cancer and Synergie Lyon Cancer Foundation.