BMS5309 · Advanced transcriptomics: Bulk, single-cell, and spatial analysis

Overview

This unit will provide you with an in-depth understanding of cutting-edge transcriptomic technologies. The central focus is on the principles and applications of bulk RNA sequencing, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics. You will explore the methodologies for data generation, processing, and analysis, gaining hands-on experience with state-of-the-art computational tools and techniques, including the use of AI and machine learning. The curriculum covers the interpretation of complex transcriptomic data, emphasising the biological insights that can be gleaned from different scales of analysis. These skills are central to a variety of research questions in genomics, biotechnology, and biomedical sciences.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. Describe and differentiate between bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics techniques.
2. Demonstrate proficiency in processing raw transcriptomic data from bulk, single-cell, and spatial RNA sequencing experiments.
3. Utilise appropriate bioinformatics tools, including Artificial intelligence (AI) and Machine Learning, for analysing transcriptomic data, including normalisation, differential expression analysis, and pathway analysis.
4. Evaluate single-cell RNA sequencing data to identify cell types, states, and trajectories using clustering, dimensionality reduction, and pseudotime analysis.
5. Interpret spatial transcriptomics data to map gene expression patterns within tissue architecture and understand the spatial context of cellular processes.
6. Translate transcriptomic findings into biological insights and hypotheses, focusing on gene function, regulation, and interaction networks.

Workload

12 hours of teacher directed study per week, including 2-4 hours of on-line lectures and 6-8 hours of workshops. 12 hours per week of self-directed study. Total per week = 24 hours

Student experience, not official rules. Nothing here changes what the Handbook says.