About INSITE

Our Mission

INSITE (Integrating scRNA-seq for T-cell Engineering) provides researchers with interactive tools to explore, predict, and optimize T-cell responses using single-cell transcriptomics data.

By aggregating publicly available T-cell scRNA-seq datasets and providing intuitive visualization and analysis tools, we aim to accelerate discoveries in immunotherapy, vaccine development, and autoimmune disease research.

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Platform Features
Explore

Interactive visualization of T-cell datasets with multi-dimensional filtering. Compare expression profiles across perturbations, cell types, tissues, and donor conditions.

  • UMAP visualization
  • Gene expression heatmaps
  • Smart filter suggestions
  • Multi-condition comparison
Predict

Simulate perturbation effects by modifying gene expression levels. Predict how T-cells would respond to genetic or pharmacological interventions.

  • In-silico gene perturbation
  • Expression level editing
  • Cell state prediction
  • Uncertainty quantification
Optimize

Define a target cell state and discover optimal gene combinations to achieve it. Find the most efficient paths to engineer desired T-cell phenotypes.

  • Target state selection
  • Combinatorial optimization
  • Multi-gene knockouts
  • Ranked intervention strategies
Data Sources
scBaseCount Repository

INSITE leverages the scBaseCount database, an AI agent-curated, uniformly processed repository of single-cell RNA sequencing data. scBaseCount aggregates data from public repositories including NCBI SRA and GEO, providing standardized metadata and consistent processing pipelines.

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Datasets

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Total Cells

2,000

HVGs Analyzed

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Perturbation Types
Processing Pipeline
1 Raw Data: Seurat v5 RDS objects from scBaseCount T-cell corpus
2 Cell Typing: SingleR + marker gene scoring (CD4, CD8, Treg)
3 Normalization: Log-normalization per 10,000 counts
4 HVG Selection: Top 2,000 highly variable genes
5 Dimensionality Reduction: PCA + UMAP for visualization
6 Export: AnnData format with unified gene space
Team
Amir Asiaee
Lead Developer & Computational Biologist
Kaveh Aryan
Platform Architect & Data Engineer
Citation & Resources
Citing INSITE
Asiaee A, Aryan K. INSITE: Integrating scRNA-seq for T-cell Engineering. DataWorks! Prize 2024 Submission. https://insiteproject.github.io
Related Resources
Contact

For questions, bug reports, or collaboration inquiries, please open an issue on our GitHub repository.

Disclaimer

INSITE is a research tool provided for educational and exploratory purposes. Results should be validated experimentally before clinical application. This platform comes with no warranty, expressed or implied. The predictions generated are based on statistical models and may not accurately reflect biological outcomes.