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.
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
Unsupervised trajectory inference and pseudotime modelling for disease progression
Identification of latent disease states and potential driver biomarkers
🛠️ Hands-on Lab: Build a multimodal variational autoencoder pipeline in Google Colab to integrate single-cell RNA-seq and ATAC-seq data from a clinical cohort and extract latent disease markers.🧰 Tools Covered: Scanpy, scVI-tools, MOFA+, Muon, Google Colab
📅 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
🛠️ Hands-on Lab: Deconvolve multicellular spatial transcriptomics spots onto high-resolution tissue histology using Cell2location and identify tumor boundary zones in Google Colab.🧰 Tools Covered: Squidpy, Cell2location, Tangram, Cellpose, Google Colab
📅 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
🛠️ Hands-on Lab: Train a Graph Neural Network on spatial tissue microenvironments in Google Colab to discover a predictive biomarker signature associated with treatment response.🧰 Tools Covered: Squidpy, LIANA+, PyTorch Geometric, Scikit-learn, Google Colab
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
