Table of contents

• ShinyMultiome.UiO for Multiome Data Visualization
• Downsampled tutorial data
• Installation
• Running ShinyMultiome.UiO
• ShinyMultiome.UiO Visualization and Functionalities
• Frequently Asked Questions
• Citations information
• Acknowledgements
• License

ShinyMultiome.UiO for Multiome Data Visualization

ShinyMultiome.UiO(ShinyMultiome User interface Open) is a user-friendly, integrative, and open-source shiny-based web app using R programming for visualization of jointly analyzed massive single-cell chromatin accessibility data (scATAC-seq) and single-cell RNA-seq(scRNA-seq) from same cells using based on Signac (Stuart et al, 2021). An example of web interface on the tutorial dataset is available at ShinyMultiome.UiO.

See our preprint

ShinyMultiome.UiO: An interactive open-source framework utilizing Seurat Objects for visualizing single-cell Multiomes. Akshay Akshay, Ankush Sharma, Ragnhild Eskeland
bioRxiv 2023.06.20.545756;
doi: https://doi.org/10.1101/2023.06.20.545756

Downsampled tutorial data

We utilized a publicly available 10x Genomic Multiome dataset for human PBMCs for analysis using and transcription factor footprinting using Cicero. The analysis performed on a test dataset of PBMC cells using Signac Signac on a test dataset of PBMC cells can be applied to users' datasets.

Installation

1. Download ShinyMultiome.UiO Source Code

• Download ShinyMultiome.UiO from the https://github.com/EskelandLab/ShinyMultiomeUiO.

  OR

• git clone https://github.com/EskelandLab/ShinyMultiome.UiO.git

2. Installation of mandatory packages

• Open the R environment or R GUI of your choice and run the following code:

  #check for devtools
  if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools")
  
  #Installing Required packages 
  install.packages(c("Seurat", "Signac", "patchwork", "ggplot2", "viridis", "shiny", "shinybusy", "shinyBS", "BSgenome.Hsapiens.UCSC.hg38"))
  

• Alternatively, execute the following commands in the command line interface:

  #Installs devtools
  Rscript -e 'install.packages("devtools",repos="http://cran.r-project.org")'
  
  #Installs BiocManager
  Rscript -e 'install.packages("BiocManager",repos="http://cran.r-project.org")'
  
  #Installs required packages 
  Rscript -e 'install.packages(c("shiny","magick","hexbin","Seurat","shinybusy","gridExtra", "grid","shinycssloaders"))' 

3. Setting up parameters

To begin, open the global.R file in a text editor and edit the following parameters:

• seuratObject: Provide the file path to the Seurat object RDS formatted file obtained after analysis in the variable named seuratObject.

  seuratObject="path/to/seurat-object.RDS"

• fragFilePath: ShinyMultiome.UiO also requires the path to the files fragments.tsv.gz and fragments.tsv.gz.tbi for the scATAC-seq data. Therefore, please provide the file path of fragments.tsv.gz in the fragFilePath variable. Additionally, ensure that fragments.tsv.gz.tbi is available in the same folder as fragments.tsv.gz.

  fragFilePath="path/to/fragments.tsv.gz"

Note: Make sure to save the changes after modifying the parameters in the global.R file.

Running ShinyMultiome.UiO

• Using Commandline

  Navigate to the folder that contains the ShinyMultiome.UiO source code and execute the following command:

  R -e "shiny::runApp('ShinyMultiomeUiO',launch.browser =TRUE)" 

• Using R GUI

  shiny::runApp('ShinyMultiomeUiO')

ShinyMultiome.UiO Visualization and Functionalities

ShinyMultiome.UiO offers various visualization plots after jointly analyzing single-cell chromatin accessibility data (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) multiome data from the same cell.

1. Clustering Tab

The Clustering tab provides a platform to visualize clustering results and investigate the relationships between cells. The two plots displays the relationship between the selected cell information and the chosen dimensionality reduction method (UMAP, LSI or PCA).

2. Feature of Interest

The Feature of Interest section allows user to explore specific features in the scRNA-seq data. Generate a plot visualizing two features using the selected plot type (Ridge, Violin, or UMAP) for four different assay types (ATAC, peaks, RNA or SCT) . Cell types can be manually added or removed for the two plots."

3. Coverage Plot

The Coverage Plot section allows user to visualize the coverage of specific genomic regions or features.

4. Footprint Plot

The Footprint Plot section allows user to analyze transcription factor binding motifs in the scATAC-seq data.

5. Downloading Plots

Within the interface, users have the flexibility to download the plots in various formats such as PDF, PNG, and TIFF. They can accomplish this by simply clicking on the Download Plot button. Additionally, users are able to customize the dimensions of the plots during both the plotting and downloading processes. This can be achieved by modifying the width and height values in the respective input area.

Frequently Asked Questions

Q: Which version of R programming is required?

• R version 4.0.0 and over is recommended.

Q: Specification of ShinyMultiome.UiO server?

RHEL system and we use SElinux, nginx, and SSL
Model name:	Intel(R) Xeon(R) Platinum 8168 CPU @ 2.70GHz
Architecture:	 x86_64
CPU op-mode(s):	32-bit, 64-bit
CPU(s):	4
CPU family:	6
RAM:	31Gi
Icon name:	computer-vm
Virtualization:	VMware
Operating System:	Red Hat Enterprise Linux 8.4 (Ootpa)
CPE OS Name:	cpe:/o:redhat:enterprise_linux:8.4:GA
Kernel:	Linux 4.18.0-305.el8.x86_64

Citations information & Contributors

Please cite ShinyMultiome.UiO article preprinted in BiorXiV preprint. ShinyMultiome.UiO: An interactive open-source framework utilizing Seurat Objects for visualizing single-cell Multiomes Akshay Akshay, Ankush Sharma, Ragnhild Eskeland bioRxiv 2023.06.20.545756; doi: https://doi.org/10.1101/2023.06.20.545756

Acknowledgements

This ShinyMultiome.UiO is developed using the Shiny package in R and makes use of various other R packages for data analysis and visualization.

License

This project is licensed under the GNU GPL3 License.