anndata

v0.12.16 safe
3.0
Low Risk

Annotated data.

🤖 AI Analysis

Final verdict: SAFE

The package exhibits very low risk indicators, with no network calls, shell risks confined to benign uses, and no signs of obfuscation or credential misuse. The only notable issue is a non-HTTPS external link, which does not strongly suggest malicious intent.

  • No network calls detected.
  • Shell commands used for benign purposes.
  • Non-HTTPS external link.
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Shell commands are used for version control and documentation updates, which align with typical package maintenance activities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows no signs of typosquatting or suspicious maintainer activity, but there's a non-HTTPS external link which could indicate a lack of attention to detail.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. helpers.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://anndata.readthedocs.io/
  • 4 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4532 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 543 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 11 unique contributor(s) across 100 commits in scverse/anndata
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • rgv) # Run towncrier subprocess.run( ["towncrier", "build", f"--version={args.version}",
  • ate a new branch + commit subprocess.run(["git", "switch", "-c", branch_name], check=True) subpro
  • branch_name], check=True) subprocess.run(["git", "add", "docs/release-notes"], check=True) pr_tit
  • s.version} release notes" subprocess.run(["git", "commit", "-m", pr_title], check=True) # push
  • if not args.dry_run: subprocess.run( ["git", "push", "--set-upstream", "origin", bra
  • hing") # Create a PR subprocess.run( [ "gh", "pr", "
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: helmholtz-munich.de>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://numfocus.org
✓ Git Repository History

Repository scverse/anndata appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Philipp Angerer, Alex Wolf, Isaac Virshup, Sergei Rybakov, Ilan Gold" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with anndata 
Create a single-cell RNA sequencing data analysis tool using the 'anndata' package in Python. Your goal is to develop a user-friendly interface where researchers can upload their single-cell RNA sequencing datasets, perform basic preprocessing steps, visualize the data, and identify clusters of similar cells. Here's a detailed breakdown of what your application should include:

1. **Data Upload**: Allow users to upload their .h5ad files, which are the native file format of the 'anndata' package. Ensure the application checks if the uploaded file is valid and readable by 'anndata'.
2. **Preprocessing**: Implement functions for common preprocessing tasks such as filtering out low-quality cells and genes, normalizing the data, and performing log-transformation.
3. **Dimensionality Reduction**: Use 'anndata' to apply dimensionality reduction techniques like PCA and t-SNE to help visualize high-dimensional data in 2D or 3D plots.
4. **Clustering Analysis**: Integrate clustering algorithms to group cells based on their gene expression profiles. Visualize these clusters in the context of dimensionality-reduced plots.
5. **Interactive Visualization**: Provide interactive visualizations where users can explore the data at different resolutions, zoom in/out, and select specific clusters for more detailed analysis.
6. **Gene Expression Heatmaps**: Enable the generation of heatmaps showing the expression levels of selected genes across all cells or specific clusters.
7. **Export Results**: Offer functionality to export the processed data back into .h5ad format, along with any derived analyses (e.g., cluster assignments).

The 'anndata' package is central to this project as it provides a structured way to store and manipulate annotated data, including both observations (cells) and variables (genes). It simplifies the integration of multiple datasets, supports complex annotations, and streamlines operations such as filtering, transformation, and visualization. By leveraging 'anndata', you'll be able to efficiently manage the large, complex datasets typical of single-cell RNA sequencing studies.