BEHAVIORAL GUIDED TRANSCRIPTOMICS
Behavior-Guided Transcriptomics (BGT) is an extension of the BEHAV3D platform that links T cell live imaging with single-cell transcriptomics to identify gene programs associated with dynamic T cell behaviors. BGT leverages BEHAV3D-processed imaging data to guide experimental design, enabling separation of T cells based on their engagement with patient-derived organoids (PDOs), followed by fluorescence-activated cell sorting (FACS) and single-cell RNA sequencing (scRNA-seq). In addition, BGT employs in silico simulations to computationally infer T cell behaviors from scRNA-seq profiles, uncovering biomarkers that distinguish highly functional from ineffective T cells. This integrated approach provides a powerful framework for understanding T cell functional heterogeneity and offers actionable insights for improving immunotherapy efficacy.
Basic Information
| Repository | https://github.com/imAIgene-Dream3D/BGT/ |
| Documentation | https://github.com/imAIgene-Dream3D/BGT/blob/HEAD/README.md |
| Main publication (DOI) | 10.1038/s41596-024-01126-4 view |
| Contact email | malieva@iib.uam.es |
| Authors/Maintainers | Maria Alieva |
| Type of application | Jupyter notebook |
| Programming languages | R |
| Keywords | Omics, Computational Methods, Resources, Transcriptomics, Data Analysis, Bioinformatics Software and Tools, Analysis packages (R, Python, etc.), Data Visualization, Functional genomics, Function prediction, Systems Biology, Integrative Analysis, Image analysis, Single-cell (scRNA-seq) |
Technical details
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| Software compatibility | Linux, Windows |
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| Type of containerization | Singularity/AppTainer |
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| Citations (last 12 months) | <100 (~3 as of 15/10/2026) |
| Citations (since first release) | <100 (7 as of 15/10/2026) |
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