feat: integrated manifold learning subworkflow and CLI scripts

[miguelrosell]

PR checklist

• [ x] This comment contains a description of changes (with reason).
• [ x] If you've fixed a bug or added code that should be tested, add tests!
• If you've added a new tool - have you followed the pipeline conventions in the contribution docs
• If necessary, also make a PR on the nf-core/scdownstream branch on the nf-core/test-datasets repository.
• Make sure your code lints (nf-core pipelines lint).
• Ensure the test suite passes (nextflow run . -profile test,docker --outdir <OUTDIR>).
• Check for unexpected warnings in debug mode (nextflow run . -profile debug,test,docker --outdir <OUTDIR>).
• Usage Documentation in docs/usage.md is updated.
• Output Documentation in docs/output.md is updated.
• CHANGELOG.md is updated.
• README.md is updated (including new tool citations and authors/contributors).

Description

The PR integrates a new Manifold Learning & Topology subworkflow into the pipeline.

New Features

• Geometry: Added PHATE and Diffusion Maps (via Scanpy).
• Topology: Added Ripser module for Persistent Homology (TDA).
• Causality: Added Trajectory Inference using PAGA + Leiden.
• Orchestration: New subworkflow subworkflows/local/manifold_learning.nf executed after the FINALIZE process.

Architecture decisions made

• Scripts: I have placed the Python scripts in a bin/ directory. They are designed as standalone CLI tools (using argparse) to ensure they can be tested independently of the Nextflow context and to keep the module files clean. I can refactor them into templates if strictly required though.
• Testing: All modules include a corresponding nf-test suite.

Changes

• Updated nextflow.config with new parameters.
• Updated conf/modules.config with publication rules.
• Modified workflows/scdownstream.nf to include the subworkflow.