Commit fde9160a authored by Bagueneau Mathias's avatar Bagueneau Mathias
Browse files

- Ajout du script install.R pour gérer l'installation des packages nécessaires...

- Ajout du script install.R pour gérer l'installation des packages nécessaires à l'utilisation de Shiny SChnurR
parent d3f3f4bc
# ~ Shiny SChnurR
![](https://zupimages.net/up/19/18/zpld.png)
</br>
**Direct Link of the Tool :** https://shiny-bird.univ-nantes.fr/jbalberge/schnurr/
# - Introduction
......@@ -13,8 +15,8 @@ Shiny SChnurR is a visualisation tool for single-cell RNA-seq analysis developpe
To use Shiny SChnurR, you first need to :
1. Install [R][R], version 3.5 or greater.
2. Install some packages (*cf Required Packages part* ).
1. Install [R][R], version 3.6 (Planting of a Tree) or greater.
2. Install some packages with the script **install.R** (*cf Required Packages part* ).
3. Download all files of this project, or clone it.
......@@ -51,7 +53,7 @@ Here the list of the required packages for Shiny SChnurR :
* org.Hs.eg.db
* tools
An installer is planned...
All of this packages are installed with the provided script **install.R**.
# - Tutorial
......@@ -72,7 +74,7 @@ Then, many information are provided :
The first graph allows you to visualise by all the factors (resolution, idents, ...) of your file.
The second one is for the features or data (all the genes repartitions, scores, ...).
The two graphs could be controlled with the panel made for. You have two graph modes : t-SNE or UMAP, and clusters informations for every factors.
The two graphs could be controlled with the panel made for. You have two graph modes : t-SNE or UMAP, and clusters information for every factors.
On the graphs, you could also select some cells, and have the percentage of your selection by the total cell number.
The data table under the graphs is pre-calculated depending the selected factor.
......
......@@ -6,7 +6,6 @@ library(dplyr)
library(sctransform)
library(ggplot2)
library(viridis)
library(shinyjs)
library(stringr)
library(plotly)
library(BiocManager)
......@@ -705,7 +704,18 @@ server <- function(input, output, session) {
## Help messages --------
observeEvent (input$help1, {
showModal(modalDialog("Hello World !", title=strong("Vizualisation page Help"), easyClose=TRUE, footer = NULL )
showModal(modalDialog("You first need to upload a .rds file, or choose one already provided as example.
Then, many information are provided :
Some files information, with genes and cells number, and the assay used.
Two graphs for the visualisation.
A data table with some pre-calculated markers.
The first graph allows you to visualise by all the factors (resolution, idents, ...) of your file.
The second one is for the features or data (all the genes repartitions, scores, ...).
The two graphs could be controlled with the panel made for. You have two graph modes : t-SNE or UMAP, and clusters informations for every factors.
On the graphs, you could also select some cells, and have the percentage of your selection by the total cell number.
The data table under the graphs is pre-calculated depending the selected factor.
It shows all significant markers, calculated with the test MAST. You could affine your marker research with the filters.
All of the outputs are exportable : in .svg for the graphs and in .csv for the table.", title=strong("Vizualisation page Help"), easyClose=TRUE, footer = NULL )
)
})
observeEvent (input$help2, {
......@@ -713,11 +723,19 @@ server <- function(input, output, session) {
)
})
observeEvent (input$help3, {
showModal(modalDialog("Hello world !", title=strong("Compare page Help"), easyClose=TRUE, footer = NULL )
showModal(modalDialog("In this page, you could compare groups from factors with each other.
You could choose multiple groups, and also add a second factor to filter with.
Then you could Find the significant markers of your selection.
Be carefull to not select redondant cells.
Like the Visualisation page, you have a Control panel to change some parameters to the graphs (like graph mode, point size, ...) and choose what you want to see (factors, features, ...).
All of the outputs are exportable : in .svg for the graphs and in .csv for the table.", title=strong("Compare page Help"), easyClose=TRUE, footer = NULL )
)
})
observeEvent (input$help4, {
showModal(modalDialog("Hello World !", title=strong("HeatMap page Help"), easyClose=TRUE, footer = NULL )
showModal(modalDialog("The HeatMap page allows you to do a HeatMap with many parameters.
You could choose a factor and the number of top genes you want to be shown.
The HeatMap also use the pre-calculated markers for each factors (like the data table from the visualisation page).
The HeatMap is exportable in .svg.", title=strong("HeatMap page Help"), easyClose=TRUE, footer = NULL )
)
})
......
#----------------------#
# Packages Installer #
#----------------------#
# Here the list of the packages required to use Shiny SChnurR
install.packages("shiny")
install.packages("shinythemes")
install.packages("dplyr")
install.packages("ggplot2")
install.packages("Seurat")
install.packages("viridis")
install.packages("shinyjs")
install.packages("plotly")
install.packages("shinydashboard")
install.packages("stringr")
install.packages("DT")
install.packages("RColorBrewer")
install.packages("data.table")
install.packages("devtools")
install.packages("BiocManager")
devtools::install_github(repo = 'ChristophH/sctransform')
BiocManager::install("clusterProfiler")
BiocManager::install("MAST")
BiocManager::install("org.Hs.eg.db")
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