Workshops and courses

Learning Pathway Bioinformatics Projects: Using deconvolution to get new insights from old bulk RNA-seq data

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Are you an educator looking for project ideas for students to practice independent enquiry and research skills? Are you a student looking for a project idea? Look no more - here, you will find a learning pathway of tutorials that can guide you through the skills to find old data and transform it into new results!

To be clear, we will only provide the methods - you will need to come up with your own research question by exploring the literature and available public datasets, apply these analyses, and interpret the results. Your research question will take the form of, "How does variable X impact the cell type proportions in issue/sample/organism Y?"

Note: You will need to be familiar with the Galaxy interface and single-cell RNA-seq analysis in general to follow this Learning Pathway. You can do so by completing the Introduction to single-cell analysis learning pathway. It would be a bonus to also complete the Beyond single cell learning pathway to reinforce that knowledge.

For support throughout these tutorials, join our Galaxy single cell chat group on Matrix to ask questions!

Keywords: advanced, single-cell

Learning objectives:

  • Apply the MuSiC deconvolution to samples and compare the cell type distributions
  • Compare the results from analysing different types of input, for example, whether combining disease and healthy references or not yields better results
  • Construct Bulk and scRNA Expression Set Objects
  • Evaluate and visualse the results of the different deconvolution methods
  • Generate psuedo-bulk data from single-cell RNA data
  • Inspect these objects for various properties
  • Measure the abundance of certain cell type cluster markers compared to others
  • Process the single-cell and psuedo-bulk data using various deconvolution tools
  • You will combine the metadata and matrix files into an AnnData or Seurat object for downstream analysis.
  • You will combine the metadata and matrix files into an ESet object for MuSiC deconvolution.
  • You will create multiple ESet objects - both combined and separated out by disease phenotype for your bulk dataset.
  • You will create multiple ESet objects - both combined and separated out by disease phenotype for your single cell reference.
  • You will manipulate the metadata and matrix files.
  • You will retrieve raw data from the EBI Single Cell Expression Atlas and Human Cell Atlas.
  • You will retrieve raw data from the EMBL-EBI Expression Atlas.
  • You will retrieve raw data from the EMBL-EBI Single cell expression atlas.

Event types:

  • Workshops and courses

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