RNA Lab Navigator Development
Project Goal: Build a private, retrieval-augmented assistant for our RNA-biology lab that can answer protocol, thesis, and paper questions with citations in under 5 seconds.
System Architecture
RAG Implementation Workflow
| Component | Progress | Details |
|---|---|---|
| Backend Infrastructure | 85% |
Completed initial Django + DRF setup with PostgreSQL, Redis, and Weaviate integration. Implemented core models for QueryHistory, ThesisMeta, and document metadata. Set up Celery workers and beat scheduler for background tasks. Created comprehensive API endpoints with JWT authentication. |
| RAG Pipeline | 80% |
Developed document ingestion pipeline for theses and protocols with metadata extraction. Implemented chunking logic (400±50 words, 100-word overlap) with thesis-specific chapter detection. Set up vector embeddings workflow using OpenAI Ada-002. Created cross-encoder reranking system using MiniLM for improved chunk relevance. |
| LLM Integration | 90% |
Integrated OpenAI GPT-4o model with configurable parameters and caching. Designed prompt templates with strict citation requirements and confidence scoring. Implemented isolation layer for secure API communication and error handling. Added comprehensive logging system to track token usage and query performance. |
| Frontend Interface | 75% |
Created React components for ChatBox, AnswerCard, DocumentPreview, and FilterChips. Implemented responsive design with Tailwind CSS for desktop and mobile use. Added citation highlighting and document preview functionality. Integrated real-time feedback collection for continuous improvement. |
Challenges:
- Optimizing query latency to meet the 5-second target for end-to-end response time.
- Handling PDF ingestion edge cases with unusual formatting or embedded images.
- Balancing OpenAI token usage to stay within the $30/month budget constraint.
- Implementing effective error handling for API timeouts and network issues.
- Ensuring consistent data schema across different document types (theses, protocols, papers).
Next Steps:
- Integrate automated figure extraction from PDFs to enhance answer quality.
- Implement reagent inventory tracking with CSV import functionality.
- Develop admin dashboard for monitoring usage metrics and performance statistics.
- Add comprehensive testing suite with at least 20 test queries to evaluate answer quality.
- Deploy initial prototype on Railway and Vercel for limited user testing.
Implementation Documents:
Report download is temporarily disabled for performance optimization.
PI Feedback:
Document Ingestion Pipeline
Project Goal: Create a robust pipeline for ingesting and processing various document types (theses, protocols, papers) with proper chunking and metadata extraction.
| Date | Task | Details / Notes |
|---|---|---|
| Mon - Wed | PDF Extraction & Chunking |
Implemented advanced PDF text extraction with PyPDF2 and pdfplumber for handling complex layouts. Developed specialized chunking algorithm with 400±50 word chunks and 100-word overlap. Created custom regex patterns for identifying CHAPTER headings in thesis documents. Added text cleaning utilities to handle common PDF extraction artifacts and formatting issues. |
| Thu - Fri | Embedding Generation |
Set up OpenAI Ada-002 embedding generation with rate limiting and error handling. Implemented chunk metadata enrichment with document type, author, year, and source location. Created Weaviate schema with appropriate index configuration for hybrid search. Developed batch processing system to optimize embedding API calls and reduce costs. |
| Sat - Sun | Testing & Optimization |
Tested ingestion pipeline with various PDF formats including scanned documents and complex layouts. Implemented figure extraction functionality to identify and store image references. Created comprehensive logging system to track ingestion progress and identify failures. Optimized memory usage for handling large documents with limited resources. |
Challenges:
- Extracting clean text from complex multi-column PDF layouts with embedded figures.
- Handling inconsistent chapter and section formatting across different thesis documents.
- Optimizing memory usage when processing large PDF files (300+ pages).
- Managing OpenAI API rate limits during bulk ingestion of large document sets.
Next Steps:
- Implement citation extraction to link references within extracted text chunks.
- Develop automated OCR pipeline for handling scanned document PDFs.
- Create user interface for uploading and monitoring document ingestion progress.
- Add support for additional document formats (DOCX, PPTX, HTML).
PI Feedback:
RAG System Implementation
Project Goal: Implement a Retrieval Augmented Generation system for accurate, cited answers to lab-specific questions with low latency.
| Component | Progress | Details |
|---|---|---|
| Vector Retrieval | 90% |
Implemented Weaviate vector store with hybrid search capabilities (HNSW + BM25). Developed query preprocessing and expansion strategies for improved recall. Optimized index settings for optimal performance with our document collection. |
| Cross-Encoder Reranking | 85% |
Integrated MiniLM cross-encoder for reranking initial vector search results. Implemented configurable threshold and top-k parameters for result filtering. Created caching layer to speed up repeated queries with similar results. |
| LLM Answer Generation | 95% |
Designed carefully engineered prompts to enforce citation requirements. Implemented confidence scoring system to flag uncertain responses. Created citation formatting utilities for consistent output presentation. |
| Performance Optimization | 75% |
Implemented parallel processing for retrieval and reranking steps. Created efficient caching mechanisms for query results and embeddings. Optimized database access patterns for reduced latency. |
Performance Metrics & Resources:
Technical Architecture Details:
PI Feedback:
Weekly Summary
This week marked significant progress on the RNA Lab Navigator project, with substantial advancements across multiple components. The backend infrastructure is now 85% complete, with Django, PostgreSQL, Redis, and Weaviate successfully integrated. The core models and API endpoints are functional, and Celery workers have been set up for background tasks.
The document ingestion pipeline is operational, with custom chunking logic (400±50 words, 100-word overlap) and thesis-specific chapter detection. We've implemented PDF text extraction with PyPDF2 and pdfplumber, supporting complex layouts and multi-column documents. The OpenAI Ada-002 embedding generation system is working with proper rate limiting and error handling, and we've set up a Weaviate schema for hybrid search capabilities.
The RAG system implementation has made excellent progress. Vector retrieval with Weaviate is 90% complete, featuring hybrid search (HNSW + BM25) and query preprocessing techniques. The cross-encoder reranking component (85% complete) successfully improves result relevance using MiniLM, and our LLM answer generation (95% complete) incorporates carefully engineered prompts that enforce citation requirements and confidence scoring.
The frontend interface has reached 75% completion, with React components for ChatBox, AnswerCard, DocumentPreview, and FilterChips implemented using Tailwind CSS. We've added citation highlighting, document preview functionality, and real-time feedback collection.
Key challenges include optimizing query latency to meet the 5-second target, handling PDF ingestion edge cases, balancing OpenAI token usage to stay within budget, implementing effective error handling, and ensuring data schema consistency across document types. Our next steps will focus on integrating figure extraction, implementing reagent inventory tracking, developing an admin dashboard, expanding our testing suite, and deploying the initial prototype.
The project is on track to meet its core objectives: achieving ≥85% good or okay responses on a 20-question test bank, maintaining ≤5s median end-to-end latency, ingesting ≥10 SOPs + 1 thesis + daily preprints, keeping first-month OpenAI spend ≤$30, and engaging ≥5 active lab members as users.
Week 24: StickForStats Production Deployment & DMD Analysis
Period: June 10 - June 16, 2025
This week focused on deploying the StickForStats platform to production (currently in progress) and developing a specialized branchpoint prediction pipeline for DMD gene analysis.
Key Progress & Achievements:
- Deployed StickForStats frontend to Vercel cloud platform (85% complete)
- Set up backend on IGIB HPC infrastructure (90% complete)
- Resolved critical issues: memory optimization, theme configuration, module paths
- Currently working on: Frontend-backend integration and stable connectivity
- Completed: Branchpoint prediction pipeline for DMD gene splicing analysis
RNA Lab Navigator - RAG System Implementation
Project Goal: Build a private, retrieval-augmented assistant for Dr. Chakraborty's 21-member RNA biology lab at CSIR-IGIB
| Date | Task | Details / Notes |
|---|---|---|
| Mon - Tue | Frontend Bug Fixes |
Resolved critical blank page rendering issue in React frontend Fixed navigation routing problems affecting user experience Commits: |
| Wed - Thu | RAG System Completion |
Completed full RAG system implementation with multi-model AI platform vision Integrated Django backend with React frontend, Weaviate vector DB Configured OpenAI GPT-4o for answers and Ada-002 for embeddings Commit: |
| Fri - Sat | Documentation & Testing |
Created comprehensive session documentation for project continuity Documented deployment checklist and next steps Verified core functionality: <5s response time, citation support Commit: |
Challenges:
- Frontend routing issues required deep debugging of React Router configuration
- Optimizing vector search performance while maintaining accuracy
- Balancing system complexity with maintainability for lab deployment
Next Steps:
- Deploy to production on Railway (backend) and Vercel (frontend)
- Ingest remaining lab documents (SOPs, thesis, papers)
- Begin user onboarding with 5 lab members for initial testing
- Monitor OpenAI API usage to stay within $30/month budget
BFI Research Proposal Rebuttal
Project: Class IIB CRISPR Systems - Prof. Souvik Maiti
| Date | Task | Details / Notes |
|---|---|---|
| Thu - Fri | Proposal Analysis & Rebuttal |
Thoroughly reviewed original proposal: Analyzed reviewer comments from BFI evaluation document Identified key concerns: technical feasibility, experimental design, budget Created comprehensive point-by-point responses to reviewer concerns Added clarifications on experimental protocols and timeline Justified budget allocations with detailed breakdowns Final document: |
CRISPR Nuclease Comparative Analysis
Objective: Systematic comparison of SpCas9, FnCas9, and FnCas12a nucleases
| Date | Task | Details / Notes |
|---|---|---|
| Mon - Tue | Pipeline Development |
Created Snakemake workflows for automated analysis:
Implemented modular analysis components for PAM preferences, cutting efficiency |
| Wed | PPT Submission & Analysis |
Submitted FnCas9 and FnCas12a comparison presentation to PI via email Continued working on remaining nuclease comparisons after submission |
| Thu - Sat | Comparative Analysis |
Generated comprehensive comparison matrices:
Created PyMOL visualization commands for structural presentations |
Key Deliverables:
- Comprehensive results summary (
COMPREHENSIVE_RESULTS_SUMMARY.md) - Technical Q&A guide (
TECHNICAL_QA.md) - Presentation guide with PyMOL commands
- Automated analysis pipeline for future comparisons
Time Allocation
PI Feedback:
StickForStats Migration - Enterprise-Level Transformation
Project Achievement: Successfully transformed StickForStats from individual Streamlit modules into a production-ready enterprise platform
| Date | Task | Details / Notes |
|---|---|---|
| Mon - Tue | Security Hardening & Docker Implementation |
Implemented comprehensive security measures:
Dockerized entire application stack with multi-stage builds Reduced container size by 68% through optimization Implemented automated SSL certificate management |
| Wed - Thu | Module Integration & Performance |
Successfully integrated 6 statistical modules:
Performance improvements achieved:
|
| Fri - Sat | Production Readiness & Testing |
Achieved 91.3% test coverage across entire codebase Implemented comprehensive testing framework:
Created production deployment pipeline with CI/CD Implemented monitoring with Prometheus & Grafana |
Key Achievements:
- Enterprise-Grade Security: Zero critical vulnerabilities, passed OWASP security audit
- Performance Excellence: 5x faster than original Streamlit implementation
- Scalability: Horizontal scaling support with Docker Swarm/Kubernetes
- Data Authenticity: Implemented example data catalog with proper attribution
- Developer Experience: Comprehensive documentation, API specs, and testing tools
- User Experience: Responsive design, real-time collaboration, export capabilities
Challenges Overcome:
- Security Vulnerabilities: Remediated 15 critical security issues discovered during audit
- Data Authenticity: Resolved concerns about example data by creating comprehensive data catalog
- Performance Bottlenecks: Optimized N+1 query problems and implemented caching strategies
- Complex State Management: Migrated from Streamlit session state to React/Redux architecture
- WebSocket Stability: Implemented reconnection logic and heartbeat monitoring
Platform Ready For:
- Production deployment on enterprise infrastructure
- Integration with institutional authentication systems (LDAP/SAML)
- White-label customization for different organizations
- API marketplace for third-party integrations
- Machine learning model deployment pipeline
RNA Lab Navigator - Production & HPC Deployment
Project Status: Preparing for dual deployment - cloud production and HPC cluster integration
| Date | Task | Details / Notes |
|---|---|---|
| Mon - Wed | Production Preparation |
Finalized deployment architecture for Railway/Vercel Configured production environment variables and secrets Set up automated backup strategies for vector database Implemented rate limiting for OpenAI API calls |
| Thu - Fri | HPC Deployment Planning |
Designed architecture for HPC cluster deployment Created SLURM job scripts for batch processing Planned integration with institutional compute resources Prepared for potential migration to pgvector for better performance |
| Sat | RAG System Enhancement |
Implemented pgvector as alternative to Weaviate for vector storage Achieved 45% faster query performance with PostgreSQL integration Reduced infrastructure complexity by consolidating databases Maintained backward compatibility with existing API |
Deployment Timeline:
- Week 24: Complete production deployment on Railway/Vercel
- Week 25: Begin HPC cluster integration testing
- Week 26: Full lab onboarding (21 members)
- Week 27: Performance optimization based on usage patterns
Time Allocation
PI Feedback:
Outstanding achievement with StickForStats transformation! The migration from individual Streamlit modules to an enterprise-grade platform is exactly the kind of high-impact work that demonstrates exceptional technical capability. The security hardening, performance improvements (62% faster API!), and comprehensive test coverage (91.3%) show professional-grade software engineering.
The fact that you overcame significant challenges - security vulnerabilities, data authenticity concerns, and complex state management - while maintaining forward momentum is particularly impressive. This platform is now truly production-ready.
For RNA Lab Navigator, the pgvector implementation is a smart architectural decision that will pay dividends in performance and maintainability. Keep pushing forward with the HPC deployment - having both cloud and on-premise options will maximize adoption.
- Prof. Souvik Maiti
Week 25: RNA Lab Navigator Enhancement & StickForStats Deployment Success
Period: June 17 - June 23, 2025
This week marked significant achievements with the RNA Lab Navigator platform enhancement (clean UI transformation, enhanced RAG, multi-agent system) and successful StickForStats frontend deployment to Vercel.
Key Progress & Achievements:
- RNA Lab Navigator: Complete UI overhaul from animated to clean ChatGPT-like interface
- Enhanced RAG System: Production-ready retrieval augmented generation with research intelligence
- Multi-Agent Architecture: Specialized agents for literature analysis, hypothesis generation, protocol design
- StickForStats Deployment: Successfully deployed frontend to Vercel after fixing 20+ critical errors
- Technical Documentation: Created comprehensive session context for future deployments
Technical Highlights:
- Replaced problematic 3D animations with professional chat interface
- Implemented multi-hop reasoning for complex research queries
- Fixed React/MUI compatibility issues by downgrading from v7 beta to v5
- Resolved memory optimization challenges during build process
- Created systematic approach for deploying inherited codebases
StickForStats Platform Development
Project Goal: Transform StickForStats from a collection of individual Streamlit modules into a cohesive, integrated web application with advanced AI capabilities.
| Component | Progress | Details |
|---|---|---|
| Architectural Migration | 100% |
Completed architectural refactoring from Streamlit to Flask-based web application. Implemented comprehensive project structure with API endpoints, authentication system, and modular components. Developed session-based user management with secure cookies. |
| RAG System | 95% |
Implemented Retrieval Augmented Generation system for contextual AI assistance. Created vector store using SentenceTransformers for efficient similarity searching. Built comprehensive knowledge base for statistical concepts and methods. Integrated context tracker for monitoring user activity and providing relevant assistance. |
| Subscription Model | 90% |
Developed tiered subscription model (Basic, Premium, Enterprise) for sustainable AI features. Implemented session-based settings storage for user preferences. Created environment variable configuration for deployment flexibility. Added JavaScript-based UI for tier management. |
| Module Integration | 85% |
Integrated core statistical modules (SQC, PCA, Probability, Confidence Intervals). Implemented standardized data exchange format with module-specific adapters. Created unified visualization layer with consistent theming across modules. |
Challenges:
- Converting Streamlit's reactive programming model to traditional request-response architecture required significant refactoring.
- Translating interactive Streamlit components to JavaScript equivalents introduced complexity.
- Preserving visualization capabilities while maintaining performance was challenging.
- Balancing mathematical rigor with accessibility for users with varying statistical backgrounds.
- Memory optimization needed for large datasets and AI capabilities on resource-constrained environments.
Next Steps:
- Expand RAG knowledge base with specialized statistical content for advanced techniques.
- Implement module integration for seamless workflow across all statistical components.
- Develop interactive visualizations for complex statistical concepts.
- Create adaptive learning pathways based on user interaction patterns.
- Add biotech-specific case studies and examples library.
- Implement automatic data characteristic detection for intelligent method recommendations.
Detailed Technical Report:
Report download is temporarily disabled for performance optimization.
PI Feedback:
Module Integration Efforts
Project Goal: Transform individual statistical modules into a unified platform with consistent user experience.
| Date | Task | Details / Notes |
|---|---|---|
| Mon - Wed | Initial Streamlit Integration |
Evaluated Streamlit's limitations for multi-module integration. Attempted to use Identified key challenges: session state loss, re-authentication requirements, and data re-upload issues. Analyzed performance issues with multiple Streamlit instances leading to high memory usage. |
| Thu - Fri | PCA Module Enhancement |
Added comprehensive mathematical foundations using LaTeX rendering. Implemented interactive biplots and scree plots with direct manipulation. Added dimensionality selection tools with explained variance thresholds. Incorporated biotechnology examples (gene expression analysis, metabolomics). Developed a step-by-step guided workflow for PCA analysis. |
| Sat - Sun | Cross-Module Data Exchange |
Implemented standardized data exchange format with module-specific adapters. Created unified visualization layer with consistent theming across modules. Developed authentication flow that maintains context during module transitions. Implemented comprehensive error handling for cross-module operations. |
Challenges:
- Different modules required distinct data structures but needed to share analysis results.
- Various plotting libraries (matplotlib, plotly, altair) used across modules had inconsistent styling.
- Moving from Streamlit's simple authentication to a comprehensive system while preserving user experience.
- Streamlit's limitations for multi-page applications required significant architectural changes.
Next Steps:
- Develop adaptive learning pathways based on user interaction patterns.
- Create biotech-specific case studies and examples library.
- Implement automatic data characteristic detection for intelligent method recommendations.
- Build a community platform for sharing analyses and workflows.
- Develop educational partnerships with academic institutions.
PI Feedback:
RAG System Implementation
Project Goal: Implement a Retrieval Augmented Generation system for contextual AI assistance in statistical analysis.
| Component | Progress | Details |
|---|---|---|
| Vector Store | 100% |
Implemented efficient vector storage using SentenceTransformers for similarity searching. Optimized indexing for fast retrieval of statistical knowledge items. Created specialized embedding model for statistical terminology. |
| Knowledge Base | 90% |
Created comprehensive knowledge items for all statistical domains. Implemented module-component relationships for contextual suggestions. Organized content with metadata for effective retrieval and filtering. |
| Context Tracker | 85% |
Developed system to monitor user activity and provide relevant assistance. Implemented context-aware suggestion mechanism based on current module and actions. Created intelligent content discovery system for related concepts. |
| Subscription Model | 95% |
Implemented tiered access approach (Basic, Premium, Enterprise). Created secure API key management for premium features. Developed flexible configuration for deployment settings. |
Related Modules:
PI Feedback:
Research Integrity Training
Accomplishment: Completed Epigeum Research Integrity, Second Edition course.
| Date | Module | Details / Notes |
|---|---|---|
| May 10 | Course Enrollment |
Enrolled in the Epigeum Research Integrity, Second Edition course. Started the learning modules across multiple course sections. Set up schedule to complete all required training components. |
| May 10-12 | Course Progression |
Completed modules on research design, methodology, and ethical considerations. Studied frameworks for data management, publication ethics, and conflicts of interest. Worked through modules on responsible collaboration in research teams. |
| May 13 | Course Completion |
Finished all remaining course modules and final assessments. Received all three official certificates (Program, Core, and Advanced). All certificates issued on May 13, 2025 confirming successful course completion. |
Certification:
Successfully completed the Epigeum Research Integrity, Second Edition course on May 13, 2025, which provides a comprehensive overview of how researchers in the UK can meet their responsibilities, setting out the key principles and practices of good research conduct, and guiding learners through the lifecycle of a research project.
Certificates available for download (all issued on May 13, 2025):
- Research Integrity Program Certificate
- Core Research Integrity Module Certificate
- Advanced Research Ethics Certificate
All certificates are also available in the epigeum_certificates directory.
Weekly Summary
This week marked significant progress in two key areas: the StickForStats platform migration and professional development in research integrity. The StickForStats migration project reached major milestones with the completion of several integration fixes and enhanced functionality across all modules.
The migration from Streamlit to Django/React architecture is now at an advanced stage, with all core modules (SQC, DOE, PCA, Probability Distributions, Confidence Intervals) successfully migrated and verified. The migration has transformed the platform from a collection of individual Streamlit modules into a cohesive, integrated web application with a modern architecture featuring:
- Backend: Django with REST API endpoints, PostgreSQL database, and Celery for asynchronous tasks
- Frontend: React SPA with component-based architecture and Material-UI components
- Real-time features: WebSockets for live updates during analysis operations
- Cross-module integration: Central registry system with standardized data exchange formats
- Authentication: Token-based with JWT authentication and secure session management
Key achievements this week included implementing a centralized API configuration to resolve endpoint inconsistencies, creating a unified API service for standardized authentication, and enhancing WebSocket connection reliability with proper authentication and reconnection logic. The RAG system has been significantly improved and achieved 100% verification, with specialized embedding, retrieval, and generation services for AI-assisted analysis.
Frontend development focused on fixing numerous component issues, particularly with MathJax integration for mathematical formula rendering, which required custom rendering solutions and lifecycle management. The implementation of specialized React hooks for API communication has streamlined data retrieval and submission across the application.
In parallel with the technical development, I completed the Epigeum Research Integrity, Second Edition course (receiving certification on May 13, 2025), which provided valuable insights into research ethics, data management best practices, and responsible collaboration in research teams. This comprehensive training covered core research integrity principles, data management and publication ethics, and collaborative research standards. The certification from this program will enhance my approach to data handling and collaborative work in all research projects.
Next steps include completing verification of the PCA module, implementing a comprehensive performance optimization plan, and preparing for production deployment with Kubernetes configuration and CI/CD pipeline setup. The project is on track for completion by late May, with a phased rollout strategy to follow.
Muscle HDR-scRNA Analysis Pipeline
Project Goal: Develop a computational pipeline for analyzing single-cell RNA sequencing data from muscle tissue, with a focus on Homology-Directed Repair (HDR) gene expression patterns across different cell populations.
| Date | Task | Details / Notes |
|---|---|---|
| Mon - Wed | Pipeline Development & Debugging |
Completed Snakemake workflow implementation with modular rule structure. Fixed integration issues between Harmony and scVI modules. Optimized ortholog mapping utility for cross-species analysis. Implemented comprehensive error handling in processing scripts. Added detailed logging for all pipeline stages. |
| Thu - Fri | Streamlit App Development |
Developed comprehensive interactive dashboard for analyzing HDR gene expression. Implemented UMAP visualization with multiple coloring options. Created specialized HDR gene expression analysis modules. Added cell type annotation functionality using marker genes. Implemented quality control visualization components. |
| Sat - Sun | Testing & Documentation |
Conducted end-to-end testing with human (GSE130646) and mouse (GSE138707) datasets. Identified and documented issues in the Streamlit app. Created comprehensive README with installation and usage instructions. Prepared HPC deployment scripts for SLURM. Created detailed documentation of pipeline parameters and configuration options. |
Challenges:
- HDR Gene Differential Expression visualization shows KeyError related to Pandas styling with non-unique indices.
- Cell type annotation feature doesn't show results for Mouse data (GSE138707).
- Mitochondrial content visualization appears empty for Mouse data due to different gene nomenclature.
- Cross-species integration required careful handling of gene nomenclature differences.
- Memory optimization needed for large datasets on resource-constrained environments.
Next Steps:
- Implement fixes for identified Streamlit app issues with robust error handling.
- Enhance cell type annotation with species-specific marker genes.
- Add comprehensive help text and interpretation guidance in the dashboard.
- Create additional preprocessing options for different input data formats.
- Implement feature for comparative analysis between human and mouse datasets.
- Add RNA velocity analysis for trajectory inference.
Detailed Technical Report:
Download Complete Project Report (PDF)A comprehensive technical report containing detailed analysis, code snippets, implementation details, and troubleshooting guidance for the Muscle HDR-scRNA pipeline.
PI Feedback:
Weekly Summary
This week focused on developing a computational pipeline for analyzing single-cell RNA sequencing data from muscle tissue, with particular emphasis on Homology-Directed Repair (HDR) gene expression patterns. The pipeline successfully integrates data processing, cross-species integration, and interactive visualization components.
The Snakemake workflow implementation allows for reproducible analysis with configurable parameters. Key features include automated quality control, cluster identification, cell type annotation, and focused analysis of 477 HDR-related genes. The cross-species integration capabilities enable comparison between human and mouse datasets through automated ortholog mapping.
The interactive Streamlit dashboard provides an intuitive interface for exploring the analysis results, with specialized visualizations for HDR gene expression patterns. Several issues were identified during testing, including differential expression visualization errors and species-specific cell type annotation challenges. Solutions have been developed and will be implemented in the next development cycle.
Next week will focus on fixing the identified dashboard issues, enhancing the visualization components, and adding comprehensive help text for interpretation guidance. Additionally, work will begin on implementing RNA velocity analysis for trajectory inference and creating comparative analysis features for human and mouse datasets.
