Introduction
As a student in bioinformatics, I am constantly dealing with data from single-cell RNA sequencing (scRNAseq). Analyzing these large datasets can be challenging, especially when it comes to visualizing and exploring clusters within the data. So, for one of my projects, I decided to build an interactive dashboard using R and RShiny to make scRNAseq analysis more accessible and intuitive.
The Single Cell RNA-seq Analysis Dashboard I built allows users to upload their own .rds files containing Seurat objects and instantly start visualizing and analyzing the data. This dashboard simplifies data processing, UMAP visualization, and gene expression analysis. I will walk through the main features of the dashboard, my design process, and the benefits of using RShiny for bioinformatics projects.
Why I Built This Dashboard
For many students and researchers, working with single-cell RNA sequencing data means navigating complex R code and data structures, especially when using tools like Seurat. Although Seurat has many great features, its interface is entirely code-based, which can be intimidating. This dashboard makes it possible to use Seurat’s main features with a point-and-click interface, so users can explore their data without needing to write long scripts.
Key Features
1. Simple File Upload: Users can drag and drop .rds files that contain Seurat objects.
2. Automated Data Processing: The dashboard handles common preprocessing steps such as normalization, scaling, PCA, and UMAP calculations.
3. UMAP Visualization: Users can explore cell clusters visually and color cells by different metadata.
4. Gene Expression Analysis: Users can check the expression levels of specific genes across cell clusters.
Dashboard Structure
The dashboard has two main parts: the UI and the Server.
• UI (User Interface): This defines the layout, appearance, and components like buttons, tabs, and dropdowns.
• Server: This is where the data processing, plotting, and functionality happen.
How the Dashboard Works
Step 1: Installing Required Packages
To get started, the necessary packages must be installed. Here is a list of the required packages and the code to install them:
install.packages(c("tools", "Seurat", "dplyr", "shinyjs", "shiny", "DT",
"shinydashboard", "shinydashboardPlus", "ggplot2",
"shinybusy", "glue", "markdown", "ggthemes"))Step 2: Loading and Running the Dashboard
After cloning the repository, the app.R file can be opened in RStudio, where it can be launched by clicking “Run App”
Once the app is running, users can see a sidebar with options to upload their .rds file and start the analysis.
Step 3: Uploading and Analyzing Data
• Uploading Data: Users need to save their scRNAseq data as a Seurat object in an .rds file format. After selecting the file, they can upload it by clicking the Upload File button. The Run button becomes active once the file is uploaded.
• Running the Analysis: Clicking Run initiates the data processing, which includes:
• Checking if the data is normalized.
• Running PCA (Principal Component Analysis).
• Running UMAP (Uniform Manifold Approximation and Projection).
While the data is being processed, a progress bar shows the analysis status, giving users feedback in real-time.
Step 4: Exploring UMAP and Gene Expression Plots
The dashboard includes tabs for both UMAP and gene expression plots, where users can explore the data further:
• UMAP Plot: This plot visualizes clusters in the data, with each point representing a single cell. Users can select different metadata categories to color the UMAP plot, allowing them to see patterns based on cell type, experimental conditions, or other relevant variables.
• Gene Expression Plot: This plot shows the expression of a selected gene across all cells. Users can pick any gene from a dropdown menu and observe its expression pattern across clusters in the UMAP plot. This feature is particularly useful for checking the expression of marker genes and identifying specific cell populations.
Lessons Learned
Working on this dashboard was a great experience and taught me a lot about data visualization and RShiny. I realized how useful it is to have an interactive tool, especially for scRNAseq data, which can be very complex. Here are a few lessons I learned:
1. RShiny Is Powerful for Bioinformatics: RShiny allows researchers to build interactive tools with minimal code, making bioinformatics more accessible.
2. UMAP Plots Are Great for Visualizing scRNAseq Data: UMAP visualizations make it easy to explore high-dimensional single-cell data and identify clusters.
3. Code Modularity: Splitting code into UI and server components not only made it easier to develop the app but also to debug and add features.
Next Steps
I plan to keep adding new features to the dashboard. Some ideas include:
• Differential Gene Expression Analysis: Comparing gene expression across different clusters.
• Enhanced Customization: Allowing users to choose color schemes or plot types.
• Download Options: Adding buttons to download plots for use in presentations or reports.
Conclusion
The Single Cell RNA-seq Analysis Dashboard was my way of simplifying scRNAseq analysis, making it accessible to more people, especially those who are new to R and bioinformatics. I am proud of this project because it bridges the gap between raw data and meaningful biological insights, all within an easy-to-use interface. Whether you are a student or researcher, I hope this tool helps you explore single-cell data in a more efficient and enjoyable way.
Result
