Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Multi-omics data integration at the single-cell level is revolutionizing our understanding of cellular heterogeneity, disease mechanisms, and therapeutic response. However, integrating high-dimensional datasets from different omics layers (e.g., genomics, transcriptomics, proteomics, epigenomics) presents significant challenges, particularly in terms of model interpretability and biological relevance. Explainable AI (XAI) methods are essential in providing transparency into the complex AI models used to analyze such data, ensuring that results are not only accurate but also biologically interpretable. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
Multi-omics data integration at the single-cell level is revolutionizing our understanding of cellular heterogeneity, disease mechanisms, and therapeutic response. However, integrating high-dimensional datasets from different omics layers (e.g., genomics, transcriptomics, proteomics, epigenomics) presents significant challenges, particularly in terms of model interpretability and biological relevance. Explainable AI (XAI) methods are essential in providing transparency into the complex AI models used to analyze such data, ensuring that results are not only accurate but also biologically interpretable.
📋 Course Objectives
1. Put biotechnology techniques to work on real datasets and case studies.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
🚀 Key Learning Outcomes
• Tangible, reproducible biotechnology work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Single-Cell Multi-Omic Modalities
• scRNA-seq, scATAC-seq and CITE-seq: what each measures and its sparsity
• Doublets, ambient RNA and dropout as technical confounders
• Paired versus unpaired multi-omic designs
Joint Representation Learning
• Batch correction and integration methods, and their over-correction risk
• Autoencoder and factor-model approaches to joint embedding
• Evaluating integration without a ground-truth alignment
Interpreting Learned Representations
• Attribution to genes, peaks and proteins driving a latent dimension
• Attention and gradient methods and their instability on sparse counts
• Distinguishing biological signal from technical covariate
Confirming an Explanation
• Marker-based sanity checks against known cell biology
• Perturbation and held-out validation of proposed mechanisms
• Reporting uncertainty in cell-type and state assignment
From Model to Biological Claim
• Regulatory inference linking accessibility to expression
• Trajectory and state-transition interpretation, and its assumptions
• Communicating an explainable result to experimental collaborators
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
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