About Workshop
This hands-on workshop teaches participants to reconstruct and interpret Gene Regulatory Networks (GRNs) from single-cell RNA-seq data. Using tools such as Scanpy, GENIE3, GRNBoost2, pySCENIC, CellOracle, NetworkX, and Cytoscape, participants will identify regulons, master regulators, disease-associated pathways, and predicted responses to gene perturbation.
Aim
To equip participants with practical skills to use AI-driven GRN inference and single-cell analysis for understanding disease mechanisms and identifying potential regulatory targets.
What Participants Will Learn
- Process and analyze scRNA-seq data.
- Infer TF–gene regulatory interactions.
- Identify regulons and master regulators.
- Perform motif-based network refinement.
- Simulate TF knockout or overexpression.
- Analyze and visualize disease-associated GRNs.
Structure
🗓️ Day 1: Transcriptomic Data to Gene Regulatory Networks
- Objective: Transform single-cell transcriptomic data into a structured gene regulatory network and infer transcription factor–target relationships using statistical and AI-based methods.
- GRN Fundamentals: Move from differential gene expression analysis toward network-level regulatory inference.
- Data Preparation & QC: Process and quality-control single-cell RNA-seq datasets using AnnData and Scanpy.
- Gene Association Analysis: Explore Pearson, Spearman, and information-based approaches for detecting gene relationships.
- AI-Based GRN Inference: Infer transcription factor–gene relationships using GENIE3 and GRNBoost2.
- Network Construction & Filtering: Build weighted regulatory networks and reduce weak or potentially spurious interactions.
🛠️ Hands-on Lab (Google Colab)
- Task: Preprocess a disease-associated single-cell RNA-seq dataset and generate an initial transcription factor–gene regulatory network.
- Tools Covered: Python, Scanpy, AnnData, GENIE3, GRNBoost2, pandas, and Google Colab.
🗓️ Day 2: TF Motifs, Regulons & Master Regulator Discovery
- Objective: Refine inferred regulatory networks using motif evidence, quantify regulon activity, and identify transcription factors that drive disease-associated cellular programs.
- Regulon Biology: Understand transcription factor–target relationships and the organization of regulatory modules.
- Motif-Enrichment Analysis: Validate candidate TF targets using cis-regulatory motif databases and enrichment analysis.
- Network Pruning: Remove indirect, weak, or unsupported regulatory interactions to improve GRN specificity.
- Regulon Activity: Calculate cell-level transcription factor and regulon activity using AUCell.
- Master Regulators: Identify disease-associated transcription factors and dominant regulatory programs across cell populations.
🛠️ Hands-on Lab (Google Colab)
- Task: Run a pySCENIC workflow, identify high-confidence regulons, calculate regulon activity, and generate transcription factor activity heatmaps.
- Tools Covered: pySCENIC, AUCell, cisTarget databases, Scanpy, Python, and Google Colab.
🗓️ Day 3: In Silico Perturbation & GRN Visualization
- Objective: Interpret regulatory networks, simulate transcription factor perturbations, and visualize disease-relevant regulatory changes for mechanistic and therapeutic research.
- Network Interpretation: Identify regulatory hubs, bottlenecks, master regulators, and candidate therapeutic targets.
- Gene Perturbation: Simulate transcription factor knockout and overexpression using CellOracle.
- Cell-State Prediction: Estimate how regulatory perturbations may redirect cellular states and trajectories.
- Network Analysis: Quantify connectivity, centrality, regulatory rewiring, and network-level changes using NetworkX.
- Publication Visualization: Export and visualize gene regulatory networks using Cytoscape-compatible GraphML and JSON formats.
🛠️ Hands-on Lab (Google Colab)
- Task: Simulate TF knockout or overexpression, compare baseline and perturbed regulatory networks, identify key regulatory shifts, and export publication-ready GRN visualizations.
- Tools Covered: CellOracle, NetworkX, Cytoscape, Scanpy, GraphML, JSON, Python, and Google Colab.
Important Dates
Registration Ends
4:30 PM
Workshop Dates
2026-08-31
5:30 PM
5:30 PM
What You Will Gain
Outcomes
- Build GRNs from single-cell transcriptomic data.
- Identify key transcription factors and regulatory programs.
- Compare regulatory activity across cell states.
- Predict effects of gene perturbations.
- Discover potential disease mechanisms and therapeutic targets.
- Generate publication-ready GRN visualizations.
Who Should Attend
- Ph.D. scholars and researchers
- Bioinformaticians and computational biologists
- Molecular and cell biologists
- Genomics and transcriptomics researchers
- Systems biology researchers
- Cancer, neuroscience, immunology, and disease researchers
- Drug discovery and biotechnology professionals
- Faculty and academicians working with single-cell or omics data