Workshops and courses

Learning Pathway Applying single-cell RNA-seq analysis

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Date: No date given

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 be introduced to a few more tools available for scRNA-seq in Galaxy. Finally, if our tool offerings are not enough for you, you will be directed towards how to use coding notebooks within Galaxy, setting you up to analyse scRNA-seq in R or python notebooks.

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 for inferring trajectories.

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

Keywords: intermediate, single-cell

Learning objectives:

  • Appraise data outputs and decisions
  • Combine data matrices from different samples in the same experiment
  • Compare the outputs from Scanpy and Monocle
  • Execute multiple plotting methods designed to identify 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
  • Follow the Monocle3 workflow and choose the right parameter values
  • Generate a cellxgene matrix for droplet-based single cell sequencing data
  • Identify decision-making points
  • Identify which operations to perform on an AnnData object to obtain the files needed for Monocle
  • Import libraries
  • Interpet trajectory analysis results
  • Interpret quality control (QC) plots to make informed decisions on cell thresholds
  • Interpret quality control plots to direct parameter decisions
  • Interpret these plots
  • Label the metadata for downstream processing
  • Launch JupyterLab in Galaxy
  • Launch RStudio in Galaxy
  • Learn about the Jupyter Interactive Environment
  • Repeat analysis from matrix to clustering
  • Repeat analysis from matrix to clustering to labelling clusters
  • 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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