RNASeq with Bioconductor Tutorial: Workspace Guide

Anton Kovalsky
  • Updated

This workspace uses JupyterLab in Terra on Azure to explore common Bioconductor packages that can be used to perform bulk RNA differential expression analyses or manipulate single-cell RNA-seq data.

Before proceeding with this tutorial, you will need to register on Terra and set up your Azure billing project.

Step 1: Set up your workspace

1.1. Go to app.terra.bio

1.2. Click the three parallel lines main menu icon in the top left to log in with your Microsoft or Google ID to use Terra on Azure. Note that if you have access to an Azure Billing project, you can log in with your Google ID
Screenshot of app.terra.bio landing page with login popup and buttons to select SSO login with either Google or Microsoft.

1.3. From the welcome screen, navigate to Workspaces
Screenshot of terra on Azure welcome page with a card to go to your workspaces page to begin your analysis journey and a blue get started button under the welcome to terra section and an explore Terra section with cards to browse data in the Terra library, view example workspaces, and view example workflows

1.4. Select the Bulk and Single-Cell RNASeq Analysis with Bioconductor workspace from the Featured Workspaces list.
Screenshot of the workspaces page in Terra on Azure with a link to the Bulk and Single-Cell RNASeq Analysis with Bioconductor workspace in the middle of the page under the featured workspaces tab

1.5. This Featured Workspace is read-only. To make your own copy of the workspace (needed to complete the tutorial), go to the top right corner and select the three vertical dots action icon.

1.6. Select clone from the drop-down menu and fill in the fields in the popup. You must use an Azure-backed Terra Billing project

What to expect

Now you have your own copy of the workspace to explore! You'll see it in Your Workspaces (note that you will be the owner). 

Step 2: Set up and launch the Jupyter service

To run a Jupyter analysis, you will need to set up the virtual machine that runs JupyterLab. See How to customize and launch Jupyter Lab for step-by-step instructions.

2.1. From the Analyses tab in your workspace, click the link to the EdgeR Jupyter notebook.

Screenshot of the Analyses tab of the clone of the bioconductor featured workspaces with two jupyter notebooks listed under Analyses

2.2. Once selected, you will be prompted to start an Azure Cloud Environment.

VM configuration

Under Cloud Compute Profile, select the Standard_DS2_v2, 2 CPU(s), 7GBs profile.

It may take 3-5 minutes to spin up.

Step 3. Run the EdgeR Notebook

What does the notebook do?

The edgeR Notebook uses the Bioconductor edgeR package to analyze synthetic bulk RNAseq gene count data. The sample data included in the analysis are read count data derived from the edgeR R package from a published study.

3.1. Once the Azure Cloud environment is completed, you'll be able to pause the environment, modify the environment using Settings, or open the newly created cloud environment with JupyterLab. 

Screenshot of the workspace with the Cloud Environment details pane highlighted. The pane has teh Jupyter logo and action icons for settings - gear icon - and to pause and open the notebook as well as the cost of the disk less than $0.01 per hour

3.2. Click to open JupyterLab with the .ipynb notebooks from the cloned workspace.

Screenshot of JupyterLab app in Terra, with the directory tree on the left and cards for nine notebooks in the center section

3.3. To start your analysis, open the edgeR.ipynb jupyter notebook from the left for editing.

3.4. Verify that the notebook is using the right kernel by checking the top right corner and bottom left corner of the browser page. It should be R.

Screenshot of the edgeR notebook in JupyterLab with an orange box around the R kernel icon in the top right corner

3.5. To change the kernel for the current notebook, click on the left bottom kernel name. This opens a window with a dropdown list to select the kernel for the edgeR.ipynb notebook.

Screenshot of the edgeR notebook in JupyterLab with a select kernel popup in the center and R in the select kernel field

3.6. Run the analysis by following the instructions in the notebook.

Step 4. Run the Single Cell Experiment Notebook

4.1. Repeat steps 3.1 - 3.6 with the next R-based notebook Single Cell Experiment.

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