# ~ 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

Shiny SChnurR is a visualisation tool for single-cell RNA-seq analysis developped in R Shiny.


# - Installation

To use Shiny SChnurR, you first need to :

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.


Then, you simply need to launch R and this command :
```r
shiny::runApp('/my/path/to/my/folder/which/contain/all/the/files')
```

The .rds files used must have been made with the analysis pipeline available at this adress : 
https://gitlab.univ-nantes.fr/E176261N/singlecell

## Conda install

To install SChnurR in a conda environment, run the following commands:

```
conda create -n schnurr -c conda-forge -c bioconda r=3.5 \
    r-shiny r-shinythemes r-seurat r-viridis r-shinyjs r-plotly \
    r-shinydashboard r-stringr r-dt r-rcolorbrewer r-data.table \
    r-biocmanager bioconductor-mast bioconductor-org.hs.eg.db   \
    bioconductor-panther.db bioconductor-topgo 
# TODO add clusterprofiler
```

## Required Packages

Here the list of the required packages for Shiny SChnurR :

* [Shiny][Shiny]
* [Seurat][Seurat]
* shinythemes
* dplyr
* ggplot2
* viridis
* shinyjs
* stringr
* plotly
* BiocManager
* shinydashboard
* shinyjs
* DT
* RColorBrewer
* MAST
* data.table
* clusterProfiler
* org.Hs.eg.db
* tools
* PANTHER.db
* topGO

All of this packages are installed with the provided script **install.R** and are up-to-date.

# - Tutorial

Many options are available into the different pages.

## Visualization Page

Once you launch the app, the first page is the Visualization page.
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 visualization.

* 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 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.
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.

## Heatmap Page

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 visualization page).

The Heatmap is exportable in .png.

## Genes Page

The Genes page allows you to do a Gene Ontology and have information about genes.
You have to choose a factor, group(s) of it, and ontology. Three type of ontology are available : Biological Process, Molecular Function and Cellular Component.

Then you will have a graph (you can also change the graph mode), and two tables.
The first table gives you the list of the genes present in your selection.
The second one gives you the ontology.

The Gene Ontology also use the pre-calculated markers for each factors (like the data table from the visualization page and the Heatmap).

All of the outputs are exportable : in .png for the graph and in .csv for the tables.

## Compare Page

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.

After this, another option and two text areas appears. The text areas are filled with the top 30 genes upregulated from one condition against the other. These areas are writable (you can add or remove some genes).
Then you could do a gene ontology with these two lists.
The results are into two tables.

Like the Visualization 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, ...).
You could also choose an ontology.

All of the outputs are exportable : in .svg for the graphs and in .csv for the tables.
You could also export the barcodes of the selected cells, in .csv.

## Pipeline Page

This page presents the different tools used in the analysis pipeline.


_____________________________

### About

**- Shiny SChnurR -**

Centre de Recherche en Cancérologie et Immunologie Nantes-Angers
UMR1232, CNRS ERL6001
IRS-UN - 8 Quai Moncousu - 44007 Nantes

**Equipe 11** 'Oncogénomique intégrative de la genèse et de la progression du Myélome Multiple'

*Mathias Bagueneau, Jean-Baptiste Alberge, Jonathan Cruard*

----

[R]: https://www.r-project.org/ "R"
[Seurat]: http://https://satijalab.org/seurat/install.html "Seurat"
[sctransform]: https://rawgit.com/ChristophH/sctransform/master/inst/doc/seurat.html "sctransform"
[Link of the tool]: https://shiny-bird.univ-nantes.fr/jbalberge/schnurr/ "Link of the tool"
[shiny]: https://shiny.rstudio.com/ "shiny"