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.
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.
1.3. From the welcome screen, navigate to Workspaces.
1.4. Select the Bulk and Single-Cell RNASeq Analysis with Bioconductor workspace from the Featured Workspaces list.
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.
2.2. Once selected, you will be prompted to start an Azure Cloud Environment.
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.
3.2. Click to open JupyterLab with the .ipynb notebooks from the cloned workspace.
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.
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.
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.