Workshops and courses

Learning Pathway Applying single-cell RNA-seq analysis in Coding Environments

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Gone is the pre-annotated, high quality tutorial data - now you have real, messy data to deal with. You have decisions to make and parameters to decide. This learning pathway challenges you to replicate a published analysis as if this were your own dataset. You will perform this analysis in coding environments hosted on Galaxy, instead of Galaxy's button-based tool interface.

The data is messy. The decisions are tough. The interpretation is meaningful. Come here to advance your single cell skills! Note that you get two options: performing the analysis predominantly in R or in Python.

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

Keywords: advanced

Learning objectives:

  • Appraise data outputs and decisions
  • Compare the outputs from Scanpy, Monocle in Galaxy and Monocle in R
  • Describe differential expression analysis methods
  • Describe the Monocle3 functions in R
  • Execute multiple plotting methods designed to maintain lineage relationships between cells
  • Explain why single cell analysis is an iterative (i.e. the first plots you generate are not final, but rather you go back and re-analyse your data repeatedly) process
  • Explain why single cell analysis is an iterative process (i.e. the first plots you generate are not final, but rather you go back and re-analyse your data repeatedly)
  • Find relevant information in GTF files for the particulars of their study, and include this in data matrix metadata
  • Generate a cellxgene matrix for droplet-based single cell sequencing data
  • Identify decision-making points
  • Identify which operations are necessary to transform an AnnData object into the files needed for Monocle
  • Import libraries
  • Interpret quality control (QC) plots to make informed decisions on cell thresholds
  • Interpret quality control plots to direct parameter decisions
  • Interpret these plots
  • Launch JupyterLab in Galaxy
  • Launch RStudio in Galaxy
  • Learn about the Jupyter Interactive Environment
  • Recognise steps that can be performed in R, but not with current Galaxy tools
  • Repeat analysis from matrix to clustering
  • Repeat analysis from matrix to clustering to labelling clusters
  • Repeat the Monocle3 workflow and choose appropriate parameter values
  • Save your notebook into your history
  • Start a notebook
  • Use get() to import datasets from your history to the notebook
  • Use put() to export datasets from the notebook to your history

Event types:

  • Workshops and courses

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