Can I use mLLMCelltype for different species?

Yes, mLLMCelltype supports multiple species including human, mouse, and other model organisms. The AI models are trained on diverse biomedical literature covering various species, making cross-species annotation possible with appropriate parameter settings.

How long does cell type annotation take?

Processing time depends on dataset size and number of models selected. Typical annotation takes 2-10 minutes for datasets with 10-50 clusters. Multi-model consensus may take longer but provides higher accuracy and confidence scores.

❓

Frequently Asked Questions

Q: What is cell type annotation and why is it important?

Cell type annotation is the process of identifying and labeling different cell types in single-cell RNA sequencing (scRNA-seq) data based on their gene expression patterns. It's crucial for understanding tissue composition, disease mechanisms, and cellular functions in biological research.

Q: How does AI-powered cell type annotation work?

AI-powered cell type annotation uses large language models (LLMs) like GPT-4, Claude, and Gemini to analyze marker gene expression patterns. These models leverage their extensive training on biomedical literature to identify cell types based on characteristic gene signatures, providing accurate and context-aware annotations.

Q: What file formats are supported for scRNA-seq data upload?

mLLMCelltype supports multiple file formats including CSV, TSV, and Excel (.xlsx) files. Your data should contain marker genes as rows and cell clusters as columns, with gene expression values or presence/absence indicators.

Q: Which AI models can I use for cell type annotation?

You can choose from 10+ AI models including OpenAI (GPT-4, GPT-4o), Anthropic (Claude 3.5), Google (Gemini 1.5), DeepSeek V3, and Chinese models like Qwen, GLM-4, MiniMax. Each model brings unique strengths to the annotation process.

Q: How accurate is multi-model consensus annotation?

Multi-model consensus annotation significantly improves accuracy by combining predictions from multiple AI models. Our benchmarks show 85-95% accuracy depending on tissue type and data quality, with consensus reducing individual model errors and providing confidence scores.

Q: Is my data secure when using the web platform?

Yes, data security is our priority. All data transmission uses HTTPS encryption, files are processed securely, and temporary data is automatically deleted after processing. We don't store your research data permanently, and you control when to download and delete results.

Q: What should I do if annotation results seem incorrect?

If results seem incorrect, try: 1) Adjusting consensus threshold settings, 2) Using additional AI models for better consensus, 3) Checking your marker gene quality and specificity, 4) Enabling discussion mode for model reasoning, 5) Consulting our troubleshooting guide for specific tissue types.

Q: Can I integrate mLLMCelltype with my existing analysis pipeline?

Yes, mLLMCelltype offers both web interface and Python package integration. You can use our API endpoints or install the mLLMCelltype Python package to integrate cell type annotation directly into your Scanpy, Seurat, or custom analysis workflows.

Get answers to common questions about cell type annotation and AI-powered analysis

Essential information to help you get started with AI-powered cell type annotation and troubleshoot common issues.

Basic Concepts

What is cell type annotation and why is it important?

Cell type annotation is the process of identifying and labeling different cell types in single-cell RNA sequencing (scRNA-seq) data based on their gene expression patterns.

Why it matters:

Disease Research: Understanding which cell types are affected in diseases
Drug Development: Identifying target cell populations for treatments
Developmental Biology: Tracking cell fate decisions during development
Tissue Function: Understanding cellular composition and interactions

How does AI-powered cell type annotation work?

AI-powered annotation leverages large language models (LLMs) trained on vast biomedical literature:

Data Input: Upload your marker gene expression data
AI Analysis: Models analyze gene signatures against biomedical knowledge
Pattern Recognition: AI identifies characteristic expression patterns
Consensus Building: Multiple models vote on cell type predictions
Confidence Scoring: Each prediction receives accuracy estimates

🔬 Technical Usage

What file formats are supported for scRNA-seq data upload?

Supported formats:

CSV files (.csv) - Comma-separated values
TSV files (.tsv) - Tab-separated values
Excel files (.xlsx) - Microsoft Excel format

Data structure requirements:

Rows: Marker genes (gene symbols)
Columns: Cell clusters or cell types
Values: Expression levels, fold changes, or binary presence/absence

Which AI models can I use for cell type annotation?

Available AI Models:

OpenAI: GPT-4, GPT-4o, GPT-4o-mini

Anthropic: Claude 3.5 Sonnet, Claude 3.5 Haiku

Google: Gemini 1.5 Pro, Gemini 1.5 Flash

DeepSeek: DeepSeek V3

Chinese Models: Qwen, GLM-4, MiniMax, StepFun

OpenRouter: Access to additional models

📊 Accuracy & Performance

How accurate is multi-model consensus annotation?

Accuracy Benchmarks:

Single Model: 75-85% accuracy
Multi-Model Consensus: 85-95% accuracy
High-Confidence Predictions: >95% accuracy

Factors affecting accuracy:

Tissue type complexity
Marker gene quality and specificity
Number of models in consensus
Data preprocessing quality

🔐 Security & Privacy

Is my data secure when using the web platform?

Security Measures:

Encryption: All data transmission uses HTTPS/TLS encryption
Temporary Storage: Files processed temporarily and auto-deleted
No Permanent Storage: We don't keep your research data
Secure APIs: All AI model APIs use secure connections
User Control: You decide when to download and delete results

🔧 Troubleshooting

What should I do if annotation results seem incorrect?

Troubleshooting Steps:

Check Data Quality: Ensure marker genes are specific and well-defined
Adjust Parameters: Lower consensus threshold or increase discussion rounds
Add More Models: Use additional AI models for better consensus
Enable Discussion Mode: Let models discuss and refine predictions
Validate Markers: Cross-check with literature or databases like CellMarker
Consider Tissue Context: Some cell types are tissue-specific

🔗 Integration & API

Can I integrate mLLMCelltype with my existing analysis pipeline?

Integration Options:

Python Package: Install via pip for direct integration
Web API: RESTful endpoints for programmatic access
Scanpy Integration: Native support for Scanpy workflows
Seurat Compatibility: Export results for R/Seurat analysis
Jupyter Notebooks: Interactive analysis examples

# Python package installation
pip install mllmcelltype

# Basic usage
from mllmcelltype import annotate_cells
results = annotate_cells(marker_data, models=['gpt-4', 'claude-3.5'])

Ready to start annotating?

Try our AI-powered cell type annotation platform now

Start Annotation Browse Resources