Regarding your request for the “Spatial_HD” project, a 20 TB quota is unfortunately far too large for us to accommodate decently on our shared infrastructure.
Visium HD datasets are indeed massive, but we need to find a more sustainable solution. Could you clarify if this 20 TB estimate represents the raw sequencing data (FASTQ) or intermediate analysis steps?
We suggest starting with a smaller allocation (e.g., 1 or 2 TB) to host your active workflows, while keeping long-term raw data archived elsewhere.
Regarding your request for the “Spatial_HD” project, a 20 TB quota is unfortunately far too large for us to accommodate decently on our shared infrastructure.
Visium HD datasets are indeed massive, but we need to find a more sustainable solution. Could you clarify if this 20 TB estimate represents the raw sequencing data (FASTQ) or intermediate analysis steps?
We suggest starting with a smaller allocation (e.g., 1 or 2 TB) to host your active workflows, while keeping long-term raw data archived elsewhere.
I already have an account so I want to add the project to it. This specific project will be run in the jupyternotebook of the iPOPUP to analyze the visium HD data using R.
My issue is when I run the scripts on the local machine, the PC gets blocked due to the huge amount of objects and analysis steps.
Therefore, it would be very helpful if you give me some recommendations on the resources I should allocate ?
I am talking about seurat objects with 35Gb of size and environments up to 70-80Gb..
That’s all done. The Spatial_HD project has been created, and your user account is now associated with it.
Regarding your question about resources: Visium HD datasets are indeed very heavy and easily saturate local machines. On our side, for the memory (RAM), please feel free to request as much as the interface/form allows you to select. Given that your Seurat objects and environments reach 70-80Gb, maximizing the allocated memory will prevent your Jupyter notebook from crashing or freezing.