Welcome to the Single-cell RNA sequencing Visualization Application - scVizApp ! This platform provides an interactive workspace for exploring summaries of cell types and clusters, interactive plots of gene expression, and access to downstream analyses.
Select an RDS or RData file using the file dialog. After loading, the following contents will be displayed:
✅ File uploaded: <file name>
Click on the link below to visualize and download scVizApp Tutorial with detailed step by step protocol to reproduce the analysis
scVizApp TutorialNavigate through the top navigation bar or action buttons to explore:
Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for uncovering cellular diversity and understanding tissue composition at high resolution. Critical information about biological differences at the cellular level can be obtained by comparing scRNA-seq datasets (treatment vs. control). We used R Shiny to create scVizApp (Single-cell Visualization Application), which enables users to easily explore and interpret scRNA-seq data, in order to make such analysis more accessible.
Our application focuses on comparative exploration by enabling users to visualize cell populations under various experimental conditions and interactively modify metadata. Regardless of the experiment model, users can create and examine Multi-Dimensional Plots, Feature Plots, Violin Plots, and Dot Plots by merely uploading an RDS/Rdata file.
Without requiring coding, scVizApp seeks to facilitate intuitive data exploration and promote deeper biological insights. This tool offers a practical solution for experimental researchers to independently investigate and interpret their single-cell datasets.
The application requires an integrated Seurat-compatible.RDS or.RData file that includes a metadata table and a normalized gene expression count matrix. The data should be preprocessed (post-integration) and ready for visualization, with components such as graphs and reductions.
scVizApp streamlines the exploration and comparison of single-cell datasets, offering five intuitive navigation sections:
1. Load Input Data: Easily upload .RDS or .RData files directly from your computer using encrypted string. All uploaded datasets are preserved, allowing quick access for comparisons or reanalysis. Navigate seamlessly through all app sections using the header navigation bar or the handy navigation buttons next to the ‘Load Data’ option.
2. Application Overview: Get a clear and succinct walkthrough of the app’s workflow and key functionalities. This is ideal for new users or collaborators who need a quick tour of scVizApp’s capabilities.
3. Cell Summary Profile: Discover the distribution of cells across experimental conditions. Explore cell counts and proportions to gain insights into sample composition and detect differences between conditions.
4. Multi-Dimensional Plots: Visualize cell cluster distributions using UMAP and t-SNE projections. These powerful 2D plots are essential for annotating cell types and revealing the complex structure of high-dimensional single-cell data.
5. Expression-Level Visualizations: Dive deep into gene expression with flexible visualization tools, including Violin Plots, Feature Plots, and Dot Plots. Select your markers of interest and conveniently download publication-quality plots for reports or presentations.
Data Access and Initialization
To ensure secure and streamlined data access, the Biocore team provisions an encrypted string for each user, granting access only to RDS files pertaining to the client’s specific analyses. Upon inputting this string, users are permitted to access all modules within the scVizApp platform.
Data Loading
Users initiate data analysis via the 'Load/Input Data' interface. This page enables users to select an RDS file from a dropdown menu populated dynamically according to the encrypted authorization string. Upon selection, the user triggers data upload by activating the ‘LOAD DATA’ button. A progress bar positioned above the button denotes the upload status. Upon successful upload, confirmation is provided via a notification displaying the completed file name (e.g., '✅ File uploaded: '). Subsequently, the application transitions automatically to the Cell Summary Profile module (Section 3.2).
User Guidance
scVizApp includes an integrated 'Overview' manual, accessible via the navigation tab, which delineates each module’s functionality. This documentation provides an abstract, detailed outline, and comprehensive descriptions, facilitating intuitive user engagement and workflow navigation.
This module empowers users to interrogate cell distribution across samples, Seurat clusters, cell types, or experimental conditions—metadata prerequisites included within the uploaded RDS file. Users may designate specific metadata variables as rows and columns to compute and visualize cell abundance and proportional distribution accordingly. Results are depicted via a stacked bar plot, illustrating distribution clarity. The module supports versatile summary calculations under varying conditions, and users may export results as CSV files or download the graphical output in PDF format. Plot aesthetics (height and width) are adjustable per user requirements.
This module facilitates visualization of dimensionality reductions present within the RDS file, including PCA, UMAP, or tSNE embeddings. Users may select from available reductions and annotations to explore cluster organization. Cluster labels are selectable through a dropdown menu, and the option to facet plots based on additional metadata is provided via the 'Clusters Split' parameter. Multi-dimensional plots are exportable in PDF format with user-defined dimensions.
Violin plots enable visualization of gene expression distributions at single-cell resolution across clusters. The user may select genes of interest (markers) via a dropdown populated by the normalized expression matrix. The x-axis can be configured to display the expression distribution across selected clusters or metadata variables. Faceting by samples or other metadata is supported to facilitate comparative expression analysis. All violin plots are downloadable in PDF format with customizable sizing.
Feature plots project marker gene expression onto a multidimensional embedding (default: UMAP), aiding spatial interpretation of expression patterns at single-cell resolution. Marker selection mirrors the interface of the Violin Plot module, and the feature plots allow faceting by experimental variables if desired. Outputs are exportable as PDF files with flexible plot dimensions.
In the Dot Plot module, users can examine relative average expression of selected markers per cluster, with dot size and color encoding percent expression and average expression levels, respectively. The platform supports comparison of multiple genes/markers, provides cell type and cell cycle phase marker options (Human only), and allows users to upload custom gene lists via Excel/CSV/TXT files for bespoke analyses. Dot plots are downloadable in adjustable PDF formats for integration into downstream reports or publications.
All plots can be resized prior to export, and downloadable options include high-resolution PDF for figures and CSV for tabular summaries. This ensures compatibility with downstream analysis pipelines and publication standards.
scVizApp is designed for users with varying expertise in single-cell analysis, providing
• Straightforward navigation via clearly labeled tabs and modules.
• Dropdown- and button-based inputs with real-time feedback.
• Automatic feature detection (e.g., reductions, markers) lowers the barrier for non-programmers.
• Seamless export options allow users to integrate figures and data into downstream publications or presentations.
• Security: Robust data access via encrypted keys ensures client privacy and analysis exclusivity.
• User-friendly: Intuitive UI/UX minimizes required training time and improves analysis speed.
• Comprehensiveness: Covers all standard visualization needs for single-cell analysis (cell summaries, reductions, expression plots).
• Customizability: Adjustable plot dimensions, faceting, and gene selection empower users to tailor analyses to specific needs.
• Interoperability: Outputs are ready for publication or further computational analysis.
• Scalability: Capable of handling varying dataset sizes and complexities, accommodating heterogeneous client requirements.
• Documentation: Built-in guidance streamlines onboarding, troubleshooting, and workflow optimization.
• Efficiency: Automatic module progression and preset workflows save time and streamline the analysis process.
• Versatile metadata-driven summaries with user-configurable axes and grouping.
Samples and conditions included in current analysis
Additional Option: Press the following button to add new column with cell type annotations/clusters/conditions to above sample description data using one of the columns as reference