BBrowserX Pro: A Comprehensive Tutorial | Updated with New UI (Nov 2025)

Introduction

This tutorial provides a comprehensive guide to using BBrowserX Pro, BioTuring’s software for analyzing and visualizing single-cell RNA sequencing (scRNA-seq) data. With a focus on the updated user interface as of November 2025, this step-by-step guide will help you navigate the software effectively to extract meaningful biological insights from your datasets.

Step 1: Exploring the Homepage

• Open BBrowserX Pro to access the homepage.
• Familiarize yourself with the layout and available options.
• Key features include:
  • Data submission options
  • Study management
  • Visualization tools

Step 2: Submitting Data

You can submit data to BBrowserX Pro through several methods:

Migrate from BBrowserX

• If you are a returning user, follow the prompts to migrate your existing data from BBrowserX.

Upload from Local Storage

• Click on the "Upload" button.
• Select your file from your local storage.
• Ensure the file format is compatible with BBrowserX Pro.

Mount Cloud Storage

• Choose the option to mount cloud storage.
• Follow the instructions to connect your cloud account (e.g., Google Drive, AWS).

Using SDK

• For developers, you can utilize the Software Development Kit (SDK) to submit data programmatically.
• Refer to the SDK documentation for detailed instructions.

Step 3: Creating a Study

• After submitting your data, navigate to the "Create Study" section.
• Input the required information for your study, including:
  • Study name
  • Description
• Save your new study to proceed.

Step 4: Quality Control and Preprocessing

• Access the QC & preprocessing tools from the main dashboard.
• Perform necessary checks on your data to ensure quality, such as:
  • Filtering low-quality cells
  • Normalizing expression data

Step 5: Understanding Your Study

• Familiarize yourself with the study used in the demo.
• Explore the summary statistics and visualizations available to get an overview of your dataset.

Step 6: Navigating the User Interface

• Take time to explore the updated user interface.
• Key areas to focus on include:
  • Navigation bar
  • Data visualization panels
  • Analysis tools

Step 7: Feature Selection and Embedding

• Use the feature selection tools to identify the most relevant genes.
• Implement embedding techniques (e.g., PCA) to reduce dimensionality.

Step 8: Performing t-SNE and UMAP

• Select the t-SNE or UMAP options for visualizing high-dimensional data.
• Adjust parameters for optimal visualization results.

Step 9: Conducting Clustering Analysis

• Use clustering algorithms to group similar cells.
• Explore various clustering methods available in the software.

Step 10: Cell Type Prediction

• Utilize the built-in cell type prediction feature.
• Analyze the predicted cell types and validate them against known markers.

Step 11: Utilizing Visualization Tools

• Access various visualization tools to present your data effectively.
• Common options include scatter plots, heatmaps, and violin plots.

Step 12: Conducting Differential Expression Analysis

• Use the differential expression analysis tools to identify significant gene expression differences.
• Interpret the results within the context of your study.

Step 13: Exploring Gene Set Enrichment Analysis

• Perform gene set enrichment analysis to discover biological pathways affected in your study.
• Review the results for insights into cellular functions.

Step 14: Analyzing Cell-Cell Communication

• Investigate cell-cell interactions using the communication analysis tools.
• Understand how different cell types interact within your dataset.

Step 15: Pseudotime Analysis

• Implement pseudotime analysis to study developmental trajectories in your data.
• Visualize the results to identify key transitions.

Step 16: Cancer Cell Prediction

• For cancer research applications, utilize the cancer cell prediction tools.
• Follow the step-by-step analysis as demonstrated in the tutorial.

Step 17: Managing Metadata

• Organize and manage metadata associated with your studies.
• Ensure that all relevant information is accurately represented.

Conclusion

In this tutorial, you learned how to effectively use BBrowserX Pro for scRNA-seq data analysis, from data submission to advanced analyses like differential expression and cell-cell communication. Experiment with the various features to leverage your datasets fully. For support, refer to the contact details provided in the software. Happy analyzing!