Improve Wilms Tumor Dataset Annotation (SCPCP000006) - explore `predicted.score` and `has_cnv.score` thresholds

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

This issue follows the PR https://github.com/AlexsLemonade/OpenScPCA-analysis/pull/844 and the 2 comments:

- https://github.com/AlexsLemonade/OpenScPCA-analysis/pull/844#discussion_r1824688578
- https://github.com/AlexsLemonade/OpenScPCA-analysis/pull/844#discussion_r1824673660

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

I would like to explore difefrent thresholds for filtering and annotating based on the `predicted.score` and `cnv.score`.
I would like to:

- [ ] improve the umpa reduction visualization with a 2-colors plot showing only one annotation and the rest in grey.

- [ ] look at the distribution of `predicted.score` for each of the `predicted.compartment` and `predicted.cell_type`. So far, we only used the `predicted.score` to select _normal_ cells (i.e. endothelial and immune cells), but don't use it to filter out cells with very low confident annotation (label as _unknown_).
- [ ] render few notebook with a cnv_threshold of 0, 1 or 2 and evaluate the identification of normal cells. I'd like to check the distribution of the `predicted.score` of endothelial, immune, _normal_ kidney and _normal_ stroma cells using each of the threshold. It can be that, due to false positive cnv, normal cells showed some infered cnv. If this is the case, we should expect to recover more normal cells with high `predicted.score` using higher `cnv_threshold`.

### What will your pull request contain?

Few changes in the `07` notebook

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

_No response_

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

_No response_

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

~ November

Start with the `07` notebook changes and review PR 844 plus its linked discussion comments for the existing annotation logic. Render versions using `cnv_threshold` values 0, 1, and 2, inspect `predicted.score` distributions by compartment and cell type, and compare the requested two-color visualizations. Done means the threshold effects are evaluated and the annotation or unknown-labeling decision is documented in the notebooks.

Written by the indexing model from the issue text.