About Workshop
This three-day workshop introduces participants to single-cell multi-omics integration, spatial transcriptomics, AI-based tissue mapping, and biomarker discovery. Through structured lectures, guided demonstrations, and Google Colab exercises, participants will learn how transcriptomic, epigenomic, protein, and spatial datasets can be analysed to identify disease-associated cellular states, tissue microenvironments, regulatory mechanisms, and predictive biomarkers for precision medicine and therapeutic research.
Aim
This workshop aims to provide participants with practical and conceptual knowledge of single-cell multi-omics integration, spatial transcriptomics, AI-based tissue analysis, and machine-learning-assisted biomarker discovery for biomedical, clinical, pharmaceutical, and translational research.
What Participants Will Learn
- Understand the principles of single-cell and spatial multi-omics technologies.
- Learn the structure and biological relevance of scRNA-seq, scATAC-seq, surface-protein, and spatial transcriptomics datasets.
- Understand preprocessing, normalization, quality control, and batch harmonization.
- Integrate multiple single-cell omics layers using AI-based latent-representation models.
- Identify disease-associated cellular states and regulatory programmes.
- Link chromatin accessibility with gene-expression patterns.
- Understand trajectory inference and pseudotime modelling.
- Compare sequencing-based and imaging-based spatial transcriptomics platforms.
- Map single-cell populations onto tissue architecture.
- Perform spatial deconvolution and cell-type abundance estimation.
Structure
Day 1: Single-Cell Multi-Omics Integration & AI Latent Representation
Core Objective: Integrate single-cell RNA, ATAC, and surface protein data using multimodal AI frameworks to uncover joint latent cell states and driver biomarkers.- Preprocessing, batch harmonization, and multimodal quality control (QC)
- Integration of single-cell RNA-seq, scATAC-seq, and surface protein datasets
- Joint latent-space embedding using Multi-Omics Factor Analysis (MOFA+)
- Variational autoencoder-based integration using scVI
- Linking chromatin accessibility to gene expression
- Inferring cell-type-specific gene-regulatory networks
- Unsupervised trajectory inference and pseudotime modelling for disease progression
- Identification of latent disease states and potential driver biomarkers
📅 Day 2: High-Resolution Spatial Mapping & AI Deconvolution
Core Objective: Map single-cell transcriptomic profiles into physical tissue architecture and deconvolve cell-type abundances using deep-learning and Bayesian models.- Introduction to high-resolution spatial transcriptomics technologies
- Comparison of sequencing-based and imaging-based spatial platforms
- Working principles of 10x Genomics Visium HD, Xenium, and MERFISH
- Deep-learning-based cell segmentation using Cellpose
- Spatial image processing and tissue-feature extraction
- Spatial spot deconvolution using Cell2location
- Mapping single-cell profiles onto spatial tissue sections using Tangram
- Identification of Spatially Variable Genes (SVGs)
- Spatial-domain detection and clustering using Squidpy
📅 Day 3: Spatial Niche Modelling & AI-Driven Biomarker Discovery
Core Objective: Model localized cell–cell communication networks, characterize spatial microenvironments, and identify predictive biomarkers for clinical translation.- Spatial ligand–receptor interaction modelling
- Cellular neighbourhood and spatial proximity analysis
- Identification of tissue niches and specialized cellular microenvironments
- Graph Neural Networks (GNNs) for modelling tissue architecture
- Modelling cell–cell communication and spatial crosstalk
- Construction of spatial graphs from transcriptomic and imaging data
- Feature selection and machine-learning pipelines for biomarker-signature extraction
- Integration of spatial and single-cell features for predictive modelling
- Validation of biomarker panels for patient stratification
- Application of biomarker signatures in treatment-response prediction and therapeutic targeting
Important Dates
Registration Ends
4:30 PM IST
Workshop Dates
2026-08-07
5:30 PM IST
5:30 PM IST
What You Will Gain
Outcomes
- Explain the principles of spatial transcriptomics and single-cell multi-omics.
- Differentiate between scRNA-seq, scATAC-seq, protein, and spatial datasets.
- Understand essential preprocessing and multimodal quality-control steps.
- Interpret integrated single-cell datasets and latent cellular representations.
- Identify disease-associated cell populations and molecular programmes.
- Understand how chromatin accessibility influences gene expression.
- Interpret cellular trajectories and disease-progression patterns.
- Compare major spatial transcriptomics technologies.
- Visualize gene-expression patterns within tissue architecture.
- Interpret spatial cell-type abundance estimates.
- Identify spatially variable genes and tissue-specific domains.
- Map tumour, immune, stromal, and disease-associated tissue regions.
- Analyse localized cell–cell communication networks.
Who Should Attend
- Graduate and postgraduate students in biotechnology, bioinformatics, molecular biology, genetics, biochemistry, life sciences, pharmacy, biomedical sciences, computational biology, data science, medicine, and allied disciplines
- PhD scholars, research fellows, project associates, and research assistants working in genomics, cancer biology, immunology, neuroscience, precision medicine, biomarker discovery, or drug discovery
- Academicians, faculty members, research supervisors, principal investigators, and laboratory professionals
- Bioinformaticians, computational biologists, molecular biologists, geneticists, and biomedical researchers
- Industry professionals from biotechnology, pharmaceuticals, genomics, diagnostics, clinical research, precision medicine, healthcare analytics, and AI-driven life-science companies
- Professionals interested in single-cell analysis, spatial transcriptomics, multi-omics integration, biomarker discovery, and computational biology