Osteosarcoma SCPCAB0027 - Tumor versus Normal cell differentiation and annotation

### Please link to the GitHub Discussion for this proposed analysis.

Discussion #1117

### Describe the goals of this analysis module.

This analysis module will -

- Read one of the 52 samples in Group ID SCPCAB0027, Project SCPCP000023 (Sample ID: SCPCS000758)
- Run InferCNV on the selected sample -
- Using the Gene order file from [Gencode Release 48 (GRCh38.p14)](https://www.gencodegenes.org/human/release_48.html)
- Using the latest `consensus cell type annotation`, and the [validation groups](https://github.com/AlexsLemonade/OpenScPCA-analysis/blob/main/analyses/cell-type-consensus/references/validation-markers.tsv) to construct normal references
- Use KMeans clustering algorithm to computationally cluster cells based on CNV score into "tumor" and "normal"
- Update the dataset with this annotation for each sample

### What software will you require?

- Programming tools - RStudio, Gitkraken, R
- Packages - infercnv

### What will your first pull request contain?

- The analysis module skeleton created by running `create-analysis-module.py`
- Some initial documentation in the README.md file

### What computational resources will you require?

- No GPU needed for this analysis module
- AWS Lightsail for Research Standard-2XL instance (8 vCPUs, 32GB RAM)
- 128 GB external storage
- Laptop - Yes, analysis can be run for one sample on a laptop

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

Target: 7/7/2025

Start by reviewing Discussion #1117, then run create-analysis-module.py to generate the analysis module skeleton and inspect the expected README.md structure. Read the consensus annotation and references/validation-markers.tsv inputs before planning the infercnv and KMeans workflow; the completed module should annotate the selected samples as tumor or normal.

Written by the indexing model from the issue text.