ISCEBERG v 2.0.1

Interactive Single Cell Expression Browser for Exploration of RNAseq data using Graphics

Overview of ISCEBERG

Here we describe our single cell data explorer. This Shiny application has been developped to analyze, visualize and extract informations from single-cell sequencing datasets. ISCEBERG can transform raw counts into filtered, normalized counts and computes clustering, UMAP projections to provide a Seurat single-cell sequencing dataset from various input formats (H5, MTX, TXT or CSV). ISCEBERG allows the use of preprocessed RDS files as input containing already a Seurat single-cell sequencing dataset. The purpose of this application is to explore much deeper and easily visualize your single-cell datasets without using R code lines. Graphics and associated tables are downloadable. To ensure Reproducibility and Traceability, preprocessed and subclusterized data are provided as Seurat objects (to be re-analyzed or re-loaded later) with a report of all command lines used to generate them.

Citation

Loïc Guille, Manuel Johanns, Francesco Zummo, Bart Staels, Philippe Lefebvre, Jérôme Eeckhoute, & Julie Dubois-Chevalier. (2022). ISCEBERG : Interactive Single Cell Expression Browser for Exploration of RNAseq data using Graphics (v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.6563734

Details

All the functionnalities are listed here. For more details see the Documentation section :

• Pre-processing (read, create and apply a first filtering on your data)
• Filtering (only if you have chosen Mtx, H5, Txt, or Csv, apply a filtering on your dataset on number of genes expressed per cell, mitochondrial DNA percentage)
• QC (QC plots about cells distribution among studied samples, projection of metadata on UMAP, cell cycle phase score computing)
• Cluster tree (create a cluster tree from different resolutions present in the object)
• Differential gene expression between clusters
• Data mining (expression plots of one or several genes with different vizualisation methods)
• Data mining for a combination of genes (expression plots of a list of genes given in parameter or with a file)
• Extract information (ex : distribution of metadata groups across clusters)
• Add annotations
• Add automatic annotations using SCINA
• Subclustering (subcluster data based on annotation or cluster)

Installation from github

Here is the procedure to install our application :

git clone https://github.com/loicguille/ISCEBERG.git

cd ISCEBERG

In order to create a docker image run the command

docker build -t image_name .(if you are already in the directory containing the dockerfile)

Once the image has been created you can run

docker run -p 3838:3838 image_name

Then type the adress in your web browser :

localhost:3838

Installation from dockerhub

A build image can be accessed on dockerhub :

docker pull loguille/isceberg:v2.0.1

and then run :

docker run -p 3838:3838 loguille/isceberg:v2.0.1

Then type the adress in your web browser :

localhost:3838

Documentation

Help is available in Documentation directory in the document Help.md.

Authors

Loïc Guille; Manuel Johanns; Francesco Zummo; Bart Staels; Philippe Lefebvre; Jérôme Eeckhoute; Julie Dubois-Chevalier

Grants

This work was supported by the Agence Nationale de la Recherche (ANR) grants “HSCreg” (ANR-21-CE14-0032-01) , “European Genomic Institute for Diabetes” E.G.I.D (ANR-10-849 LABX-0046), a French State fund managed by ANR under the frame program Investissements d’Avenir I-SITE ULNE / ANR-16-IDEX-0004 ULNE, by grants from the Fondation pour la Recherche Médicale (FRM : EQU202203014645) and by European Commission

Agence Nationale de la Recherche:

• EGID - EGID Diabetes Pole (10-LABX-0046) 

European Commission:

• ImmunoBile - Bile acid, immune-metabolism, lipid and glucose homeostasis (694717)