Using Spatial-Cell-ID computational resources through IFB cloud

A step by step guide

For analyzing single-cell RNASeq and Spatial Transcriptomics data, Spatial-Cell-ID users can exploit CBMPsmn dedicated resources through the IFB Cloud Biosphere.

For example, for using Seurat and scanpy/squidpy frameworks (in R and python respectively), pre-configured virtual machines (VMs) can be used, where all the computational environments have already been set up.

Below are the instructions on how to proceed.

Join the Spatial-Cell-ID Group on IFB cloud

  1. Go to https://biosphere.france-bioinformatique.fr/

  2. Click on Sign in at the top right.

  3. Click Sign in again.

  4. Click on Accept conditions.

  5. Select your own institution.

  6. Log in with your institution’s username and password.

  7. Fill in the information: Last Name, First name, city, and postal code. Leave the other information as default and accept.

  8. You will be directed to a new page. Click on the small person icon at the top right, then select Groups.

  9. Click on Join a group in the tabs at the top left.

  10. Search for the group Spatial-Cell-ID and click on the + button to apply.

  11. That’s it! Now you have to wait for your application to be validated.

Once your application will be validated, you can deploy a Virtual Machine from Biosphere.

How to deploy a Virtual Machine (VM)

  1. Click on RAINBio at the top left.

  2. Select the appliance that you want (for example Spatial-Cell-ID Jupyter for working with Python, and Spatial-Cell-ID-RStudio for working with R or with the terminal. Note: Spatial-Cell-ID-RStudio is not yet available; we are awaiting validation by IFB. In the meanwhile, you can use UE NGS-ENS Lyon for working with R or the terminal).

  3. Click RUN at the top right and then select ADVANCED CONFIGURATION AND START.

  4. Choose a name for your VM, select Spatial-Cell-ID as the Group to use, meso-psmn-cirrus as the Cloud, and choose the appropriate Cloud flavor for your analysis (I would suggest standard.4c16g, which should be sufficient for most of the analysis).

  5. Go to myVM at the top right.

  6. Wait for the appliance to start. At some point, you will see that Access will have two options: https and params. Click on params to view the user ID and password, then click on https to connect to the virtual machine using the user ID and password you just obtained.

  7. You could also need to deploy a whole remote desktop computer with Ubuntu installed on it (this is the case, for example, of the appliance Ubuntu 22.04 Desktop. Then, you will need the client X2Go. Please, refer to the IFB documentation on how to do that).

  8. That’s it! Your virtual machine is ready, and you will find everything in your environment and you will have access to a high performing computer.

Important remarks

  • When you terminate your virtual machine by clicking on Terminating deployments, your data will be lost. You can shut down your local computer and close the web browser tab where the virtual machine is running without losing your data. You can access your virtual machine again in the MyVM section. However, if you terminate the deployment without saving your data and scripts, everything will be lost. Uploading and downloading data is intuitive, but feel free to ask for help if needed.

  • Please keep in mind that these computational resources are not for storing data, but only for computing. When you finish your analysis, save your data and terminate the deployment of the virtual machine. This will save energy and resources.

Further information

A step-by-step guide on how to use Git directly from the virtual machine will be released as soon as possible.