AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activity and poses minimal risk. The only concern is the author having only one package, which may suggest a new or less active maintainer.
- No network calls
- No shell execution
- No obfuscation
- No credential harvesting
- Single package by author
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands, which is typical for most Python packages.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting no immediate threat to stored secrets.
- Metadata: The author has only one package, which might indicate a new or less active maintainer, but no other red flags are present.
Package Quality Overall: Medium (6.6/10)
✦ High
Test Suite
9.0
Test suite present — 7 test file(s) found
7 test file(s) detected (e.g. test_implicit_scale.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://github.com/Starlitnightly/anndata-oomDetailed PyPI description (21078 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
101 type-annotated function signatures detected in source
✦ High
Multiple Contributors
8.0
Active multi-contributor project
4 unique contributor(s) across 46 commits in Starlitnightly/anndata-oomSmall but multi-author team (3–4 contributors)
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository Starlitnightly/anndata-oom appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "omicverse contributors" 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 anndataoom
Create a mini-application called 'MemoryEfficientGeneAnalyzer' using Python that leverages the 'anndataoom' package to efficiently handle large-scale single-cell RNA sequencing data without running into memory issues. This application will serve as a tool for biologists and researchers who work with massive datasets but have limited computational resources. The application should include the following functionalities: 1. **Data Importation**: Allow users to import single-cell RNA sequencing data in the AnnData format. The application should support various file formats commonly used in single-cell RNA sequencing studies. 2. **Data Preprocessing**: Implement basic preprocessing steps such as filtering cells based on gene expression levels, normalizing the data, and performing dimensionality reduction techniques like PCA (Principal Component Analysis). 3. **Gene Expression Analysis**: Provide tools for analyzing gene expression patterns. Users should be able to query the dataset for specific genes, visualize their expression across different samples, and compare their expression levels between conditions. 4. **Visualization**: Integrate visualization capabilities to help users understand the structure of their data. Include options for generating scatter plots based on principal components, heatmaps showing gene expression levels, and interactive dendrograms for hierarchical clustering. 5. **Out-of-Memory Processing**: Utilize the 'anndataoom' package to ensure that the application can handle datasets larger than available RAM by processing data in chunks. This feature is crucial for ensuring the application remains responsive and usable even with very large datasets. 6. **User Interface**: Develop a simple command-line interface (CLI) for interacting with the application. The CLI should guide users through importing their data, selecting preprocessing steps, and choosing analysis and visualization options. 7. **Documentation**: Provide comprehensive documentation explaining how to install and use the application, including examples and tutorials. The application should demonstrate the power of 'anndataoom' in managing large biological datasets efficiently and showcase its potential in real-world research scenarios.