AI Analysis
Final verdict: SAFE
The package shows minimal risks across all categories with no network calls, shell executions, obfuscations, or credential harvesting attempts detected. The metadata risk is slightly elevated due to the maintainer having only one package, but this alone does not suggest any malicious intent.
- Low risk in all categories except metadata.
- Maintainer has only one package, which may indicate new or less active account.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
- Shell: No shell execution detected, indicating the package does not execute external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which might indicate a new or less active account, but no other red flags are present.
Package Quality Overall: Medium (7.0/10)
✦ High
Test Suite
9.0
Test suite present — 5 test file(s) found
Test runner config found: conftest.pyTest runner config found: pyproject.toml5 test file(s) detected (e.g. conftest.py)
✦ High
Documentation
9.0
Well-documented package
Documentation URL: "Documentation" -> https://annbatch.readthedocs.io/2 documentation file(s) (e.g. conf.py)Detailed PyPI description (6370 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
152 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
6 unique contributor(s) across 100 commits in scverse/annbatchActive 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
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: scverse.org>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository scverse/annbatch appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Felix Fischer, 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 annbatch
Create a data analysis tool that leverages the 'annbatch' package to efficiently process large-scale single-cell RNA sequencing datasets stored in AnnData format. Your goal is to develop a user-friendly application that allows researchers to explore, visualize, and analyze complex biological data. Here’s a step-by-step guide on how to approach this project: 1. **Setup Environment**: Begin by setting up a Python environment. Install necessary packages including 'annbatch', 'scikit-learn', 'pandas', 'matplotlib', and 'seaborn'. 2. **Loading Data**: Use 'annbatch' to load your dataset in batches. This will help manage memory usage when dealing with large files. 3. **Data Preprocessing**: Implement functions to preprocess the data. This includes normalization, filtering out low-expressing genes, and handling missing values. 4. **Feature Selection**: Utilize dimensionality reduction techniques such as PCA or t-SNE to reduce the number of variables while retaining key information. 5. **Visualization**: Create interactive visualizations of the reduced dataset using tools like Plotly or Bokeh. Allow users to explore different dimensions and clusters. 6. **Clustering Analysis**: Apply clustering algorithms (e.g., K-means, DBSCAN) to identify distinct cell populations within the dataset. 7. **User Interface**: Develop a simple GUI using Tkinter or Streamlit to make the application more accessible. Users should be able to upload their own datasets, select preprocessing methods, and view results. 8. **Documentation & Testing**: Ensure thorough documentation and testing of your code. Include examples and explanations for each feature implemented. The 'annbatch' package plays a crucial role in managing the loading process, allowing efficient access to large datasets without overwhelming system resources. By following these steps, you'll create a powerful tool for researchers studying single-cell transcriptomics.
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