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Commit bdf0189e authored by Rachel  LEGENDRE's avatar Rachel LEGENDRE
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rename package

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#' Provide R tools and an environment for the statistical analysis of ChIP-Seq data: load and clean data, produce figures, perform statistical analysis/testing with DESeq2 or edgeR, export results and create final report #' Provide R tools and an environment for the statistical analysis of ChIP-Seq data: load and clean data, produce figures, perform statistical analysis/testing with DESeq2 or edgeR, export results and create final report
#' @title Statistical Analysis of RNA-Seq Tools #' @title Statistical Analysis of ChIP-Seq Tools
#' @author Maelle Daunesse, Marie-Agnes Dillies and Hugo Varet #' @author Maelle Daunesse, Marie-Agnes Dillies and Hugo Varet
#' @docType package #' @docType package
#' @name ChIPuana-package #' @name ChIPflow-package
NULL NULL
...@@ -3,7 +3,7 @@ ...@@ -3,7 +3,7 @@
# ========================================================================== # ==========================================================================
.onAttach = function(libname, pkgname) { .onAttach = function(libname, pkgname) {
msg <- c("----------------------------------------------", msg <- c("----------------------------------------------",
paste0("Welcome to ChIPuanaR version ", packageVersion("ChIPuanaR"))) paste0("Welcome to ChIPflowR version ", packageVersion("ChIPflowR")))
msg <- c(msg,"----------------------------------------------") msg <- c(msg,"----------------------------------------------")
msg <- strwrap(msg, exdent=4, indent=4) msg <- strwrap(msg, exdent=4, indent=4)
packageStartupMessage(paste(msg, collapse="\n"), appendLF=TRUE) packageStartupMessage(paste(msg, collapse="\n"), appendLF=TRUE)
......
# ChIPuanaR # ChIPflowR
How to install ChIPuanaR? How to install ChIPflowR?
------------------------- -------------------------
In addition to the ChIPuanaR package itself, the workflow requires the installation of several packages: DESeq2, limma, knitr... (all available online, see the dedicated webpages). In addition to the ChIPflowR package itself, the workflow requires the installation of several packages: DESeq2, limma, knitr... (all available online, see the dedicated webpages).
To install the ChIPuanaR package from GitLab, open a R session and: To install the ChIPflowR package from GitLab, open a R session and:
- Install devtools with `install.packages("devtools")` (if not installed yet) - Install devtools with `install.packages("devtools")` (if not installed yet)
- Notes: - Notes:
...@@ -13,4 +13,4 @@ To install the ChIPuanaR package from GitLab, open a R session and: ...@@ -13,4 +13,4 @@ To install the ChIPuanaR package from GitLab, open a R session and:
- Some users may have to install the pandoc and pandoc-citeproc libraries to be able to generate the final HTML reports - Some users may have to install the pandoc and pandoc-citeproc libraries to be able to generate the final HTML reports
- For Windows users only, install [Rtools](https://cran.r-project.org/bin/windows/Rtools/) or check that it is already installed (needed to build the package) - For Windows users only, install [Rtools](https://cran.r-project.org/bin/windows/Rtools/) or check that it is already installed (needed to build the package)
- Run `devtools::install_gitlab(repo="hub/chipuanar", host="gitlab.pasteur.fr", build_vignettes=TRUE)` - Run `devtools::install_gitlab(repo="hub/chipflowr", host="gitlab.pasteur.fr", build_vignettes=TRUE)`
--- ---
#logopath: "inst/Logos_LogoC3BI.png" #system.file("Logos_LogoC3BI.png", package = "ChIPuana") #logopath: "inst/Logos_LogoC3BI.png" #system.file("Logos_LogoC3BI.png", package = "ChIPflow")
title: "ChIPuana differential analysis report" title: "ChIPflow differential analysis report"
date: '`r Sys.Date()`' date: '`r Sys.Date()`'
output: output:
html_document: html_document:
#css: ""#system.file("vignette.css", package = "ChIPuana") #css: ""#system.file("vignette.css", package = "ChIPflow")
toc: TRUE toc: TRUE
toc_depth: 2 toc_depth: 2
toc_float: toc_float:
...@@ -18,15 +18,14 @@ bibliography: bibliography.bib ...@@ -18,15 +18,14 @@ bibliography: bibliography.bib
csl: medecine-sciences.csl csl: medecine-sciences.csl
--- ---
This report is generated by ChIPuanaR, and R package included in ChIPuana a Snakemake pipeline for ChIP-seq data. ChIPuanar is an adaptation of the SARTools R package [@Varet2016] and is freely available at https://gitlab.pasteur.fr/hub/chipuanar. If you use ChIPuana and/or ChIPuanaR please cite: M. Daunesse, R Legendre, H. Varet, A. Pain, C. Chica. 2021. _ChIPuana: from raw data to epigenomic dynamics_, bioRxiv. This report is generated by ChIPflowR, and R package included in ChIPflow a Snakemake pipeline for ChIP-seq data. ChIPflowR is an adaptation of the SARTools R package [@Varet2016] and is freely available at https://gitlab.pasteur.fr/hub/chipflowr. If you use ChIPflow and/or ChIPflowR please cite: M. Daunesse, R Legendre, H. Varet, A. Pain, C. Chica. 2021. _ChIPflow: from raw data to epigenomic dynamics_, bioRxiv.
```{r echo=FALSE, results="asis", results="hide", message=FALSE, warning=FALSE} ```{r echo=FALSE, results="asis", results="hide", message=FALSE, warning=FALSE}
# Package # Package
library(knitr) library(knitr)
library(kableExtra) library(kableExtra)
library(ChIPuanaR) library(ChIPflowR)
### INPUTS ### INPUTS
# setwd("~/work/projects/ChIPuana/chipuanar/inst")
source("config.R") source("config.R")
data <- read.table(file = file, header=TRUE) data <- read.table(file = file, header=TRUE)
counts <- as.matrix(data[,-c(1:6)]) counts <- as.matrix(data[,-c(1:6)])
...@@ -83,7 +82,7 @@ for (name in names(resAnDif$results)){ ...@@ -83,7 +82,7 @@ for (name in names(resAnDif$results)){
# 1. Introduction # 1. Introduction
The differential analysis is performed to detect differentially marked/bound peaks between the biological conditions. Two approaches to model variability (DESeq2 and limma) as well as linear and nonlinear normalisation methods (scalar, quantile, cyclicloess, spike-in) are available to perform the DA. Using the count matrix generated by featureCounts, ChIPuanaR allows to carry out the quality assessment of the data using dedicated plots, to run the DA and to export (i) a Rmarkdown HTML report describing the analysis process and (ii) the tables containing the differentially marked/bound peaks. The differential analysis is performed to detect differentially marked/bound peaks between the biological conditions. Two approaches to model variability (DESeq2 and limma) as well as linear and nonlinear normalisation methods (scalar, quantile, cyclicloess, spike-in) are available to perform the DA. Using the count matrix generated by featureCounts, ChIPflowR allows to carry out the quality assessment of the data using dedicated plots, to run the DA and to export (i) a Rmarkdown HTML report describing the analysis process and (ii) the tables containing the differentially marked/bound peaks.
# 2. Description of raw data # 2. Description of raw data
......
% Generated by roxygen2: do not edit by hand % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ChIPuana-package.r % Please edit documentation in R/ChIPflow-package.r
\docType{package} \docType{package}
\name{ChIPuana-package} \name{ChIPflow-package}
\alias{ChIPuana-package} \alias{ChIPflow-package}
\title{Statistical Analysis of RNA-Seq Tools} \title{Statistical Analysis of ChIP-Seq Tools}
\description{ \description{
Provide R tools and an environment for the statistical analysis of ChIP-Seq data: load and clean data, produce figures, perform statistical analysis/testing with DESeq2 or edgeR, export results and create final report Provide R tools and an environment for the statistical analysis of ChIP-Seq data: load and clean data, produce figures, perform statistical analysis/testing with DESeq2 or edgeR, export results and create final report
} }
......
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