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Approx. x hours to complete
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Single-cell transcriptomics is a powerful tool to study the heterogeneity of the cellular transcriptome at single-cell levels. This course aims to equip participants with essential skills to process RNA-sequencing reads from single-cell transcriptomics experiment and to perform downstream analyses of the single-cell gene expression data. This course is designed for participants with some experience in R programming and running command-line programs. We will be using published single-cell RNA-sequencing datasets to perform quality control, data normalisation, cell clustering, differential expression and trajectory analyses.
This course is for you if
- You have basic knowledge in using command-line terminal and in running commands in R
- You work with, or about to work with, single-cell transcriptomics experiments
- You need to analyse your own single-cell transcriptomics dataset, or analyse publicly available datasets
- You require flexible learning format
Learning objectives
- A broad understanding of different single-cell transcriptomics experiments
- Confidently process and analyse single-cell transcriptomics datasets
- Being able to formulate research questions on single-cell transcriptomics analysis and finding answers to these questions
By the end of the course you will be able to:
- Identify different single-cell sequencing methods and its advantages and disadvantages
- Explain the various applications of single-cell sequencing experiments
- Process raw sequencing data from 10x genomics for gene expression analysis
- Analyse gene expression data in R using the Seurat package
- Perform single-cell analysis workflow which includes quality control, data normalisation, data clustering, differential expression analysis and trajectory analysis.
- Visualise single-cell expression data