Integration of the SCPCP000006 Wilms tumor dataset

### If you are filing this issue based on a specific GitHub Discussion, please link to the relevant Discussion.

This issue is related to the steps 6 and 7 I described in my proposed analysis https://github.com/AlexsLemonade/OpenScPCA-analysis/discussions/635#discussion-6961060

### Describe the goals of the changes to the analysis module.

Step 6 – validation by integration of the 40 samples
I would like to integrate the 40 snRNA-Seq using scVI or harmony, perform dimensional reduction and clustering. This will allow to validate our annotations, as cells from the same cell type should cluster together. At the sample level, normal and cancer cells fro the same histology cluster together, this might not be the case in the integrated dataset (hopefully 🤞 ).

Step 7 – identification of marker genes for each cell subtype using differential expression analysis
Finally, we would like to provide the WT community with specific and universal marker genes for a rapid identification of the different cell types found within the tumors. To do so, we will use pseudobulk differential expression analyses ([DElegate package](https://github.com/cancerbits/DElegate)) to find markers of the different cell types using the function FindAllMarkers2 (default parameters, patient as replicate). We would like to even further validate candidate Wilms tumor marker genes in the VISIUM data and/or in FFPE sample (IHC) and _in vitro_ models (IF).

Additionally, we could compare relapse and non-relapse samples per cell type using the function findDE (replicate_column = "patient", method = “edger”) to evaluate if a specific phenotype within the cancer cells or the microenvironment could indicate relapse in WT.

### What will your pull request contain?

- [ ] a script for the integration of the 40 samples
- [ ] a notebook for the exploration of the clustering, marker genes
- [ ] notebooks performing and exploring differential expression analyses (normal versus cancer, histologies between them, relapse versus non-relapse, etc). Idea would be one notebook per question (and PR!) 💡

### Will you require additional software beyond what is already in the analysis module?

`scvi` integration requires conda environment.

### Will you require different computational resources beyond what the analysis module already uses?

The integration of the 40 samples will require quite some ressource and might not run in cli.

### If known, when do you expect to file the pull request?

_I have quite some wet lab work pending and I am not sure when I'll be able to focus on the described follow-up analysis_, maybe somewhere in December.

But I like to do these analyses, that will hopefully allow improved marker identification of cancer versus normal and for specific histological subtypes (epithelial, blastemal, stromal), which is crucial for our future research and would be valuable for the Wilms tumor community.

Part of the work might be out of the scope of the Open-ScPCA project, happy to discuss with you if/how you like to continue the analysis!

Start by reviewing Discussion 635 and the proposed steps 6 and 7. The work described is an integration of 40 samples using scVI or Harmony, followed by clustering, marker-gene analysis, and differential-expression notebooks using DElegate. Done would include the proposed integration script and analysis notebooks, but the repository files and final scope are not specified.

Written by the indexing model from the issue text.