Commit 29ac9794 authored by fsoubes's avatar fsoubes

add matrix

parent 037145f3

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Package: MATRiX
Type: Package
Title: Heatmap generator
Version: 0.1.0
Author: Yannick Lippi and Franck Soubès
Maintainer: Franck Soubès <Franck.Soubes@inra.fr>
Description: Treat microarray data
License: MIT
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.0.1
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# MATRiX
# MATRiX is a shiny application for Microarray Analysis on Transcriptomic impact of Xenobiotics
## Contents
- [Introduction](#introduction)
- [Installation](#installation)
- [Contact](#contact)
- [Acknowledgements](#acknowledgements)
## Introduction
This project initiate by Yannick Lippi aims to facilitate access to biologists in order to publish graphs such as heatmap, PCA or Venn diagram related to specific data produced by TRiX's team.
MATRiX is an application dedicated to DNA chip analysis, this application incorporates quality control with Principal components analysis to summarize microarray and differential analysis with various methods such as Venn diagram, Heatmap clustering and GO Enrichment analysis by querying the DWS (DAVID WEB SERVICES).
MATRiX app is working with specific data produced by the limma package, resulting p-values are adjusted according to the Benjamini and Hochberg procedure [Benjamini and Hochberg 1995].
PCA is computed with the FactoMineR package and the plot is produced with the factoextra package, for the Heatmap and Venn diagram the graphs are obtained respectively with the gplots and VennDiagram package, those packages are available on CRAN
This application works only with specific data produced by the plateau TRiX, you can check the example file (MA_Trix_App/downloadData.zip)
Here's the global workflow passing by the experiment to the visualization:
![](./www/whatmaen.png)
and also a [video Presentation](https://www.youtube.com/watch?v=lfI0zRYzeJs)
## Installation
MATRiX is available for R>3.5.0. The installation, download and execution can all be performed with a small R script :
```
## Download RDAVIDWebService
source("https://bioconductor.org/biocLite.R")
biocLite("RDAVIDWebService")
## Load RDAVIDWebService
library(RDAVIDWebService)
## Load shiny packages
if(!require('shiny')){
install.packages('shiny')
library(shiny)
}
# Install dependencies, download last version of MATRiX from github and run matrix in one command :
runGitHub('fsoubes/MA_Trix_App')
```
You can also run the app as following :
```
git clone https://github.com/fsoubes/MA_Trix_App
chmod +x ./cmd.sh
./cmd.sh
```
## Contact
Here are our mail [Franck Soubès] (franck.soubes@inra.fr) or [Yannick Lippi] (yannick.lippi@inra.fr) for any questions, suggestions or if you need help to use MATRiX, dont hesitate to contact us.
## Acknowledgements
The main contributors to MATRiX:
Yannick Lippi, (Initiator, beta-testing, feature suggestions),
Franck Soubès, (Coding, Unit testing, documentation, packaging, feature suggestions),
Didier Laborie, (Virtual machine and DNS),
TOXALIM Team: BioToMyc & TIM, (beta-testing, feature suggestions)
Claire Neyles, (suggestions)
File added
library(DiagrammeR)
mermaid("
gantt
dateFormat YYYY-MM-DD
title A Very Nice Gantt Diagram
section Basic Tasks
This is completed :done, first_1, 2014-01-06, 2014-01-08
This is active :active, first_2, 2014-01-09, 3d
Do this later : first_3, after first_2, 5d
Do this after that : first_4, after first_3, 5d
section Important Things
Completed, critical task :crit, done, import_1, 2014-01-06,24h
Also done, also critical :crit, done, import_2, after import_1, 2d
Doing this important task now :crit, active, import_3, after import_2, 3d
Next critical task :crit, import_4, after import_3, 5d
section The Extras
First extras :active, extras_1, after import_4, 3d
Second helping : extras_2, after extras_1, 20h
More of the extras : extras_3, after extras_1, 48h
")
df <- data.frame(task = c("Recherche bibliographique", "Elaboration d'un cahier des charges", "Conception de l'architecture",
"Implémentation sous R", "Implémentation sous Shiny",
"Déploiement de MATRiX en ligne", "Maintenance de MATRiX", "Optimisation du code", "Rédaction du rapport de stage"),
status = c("active", "active", "active"),
pos = c("first_1", "first_2", "first_3","first_4", "first_5","first_6", "first_7", "first_8", "first_9"),
start = c("03-05", "03-05", "after first_1"),
end = c("08-31", "3d", "5d"))
View(df)
df <- data.frame(task = c("Recherche bibliographique", "Elaboration d'un cahier des charges",
"Conception de l'architecture",
"Implémentation sous R", "Implémentation sous Shiny",
"Déploiement de MATRiX en ligne", "Maintenance de MATRiX", "Optimisation du code", "Rédaction du rapport de stage"),
status = c("active", "active", "active"),
pos = c("first_1", "first_2", "first_3","first_4", "first_5","first_6", "first_7", "first_8", "first_9"),
start = c("03-05", "03-05", "after first_1"),
end = c("08-31", "3d", "5d"))
library(reshape2)
library(ggplot2)
rm(list = ls())
task1 <- c('Recherche bibliographique', '2018-03-05', '2018-03-25')
task2 <- c('Elaboration d"un cahier des charges', '2018-03-25', '2018-04-15')
task3 <- c('Conception de l"architecture', '2018-04-15', '2018-04-30')
task4 <- c('Implémentation du code source', '2018-04-20', '2018-06-30')
task5 <- c('Déploiement de MATRiX en ligne', '2018-05-10', '2018-06-20')
task6 <- c('Bêta test', '2018-05-20', '2018-05-22')
task7 <- c('Optimisation/Correction des bugs', '2018-06-20', '2018-08-20')
task8 <- c('Maintenance de MATRiX', '2018-06-20', '2018-07-01')
task9 <- c('Rédaction du rapport de stage', '2018-07-15', '2018-08-20')
df <- as.data.frame(rbind(task1, task2, task3, task4,task5, task6, task7, task8, task9))
names(df) <- c('task', 'start', 'end')
df$task <- factor(df$task, levels = df$task)
df$start <- as.Date(df$start)
df$end <- as.Date(df$end)
df_melted <- melt(df, measure.vars = c('start', 'end'))