AI Analysis
Final verdict: SUSPICIOUS
The package has a moderate risk score due to incomplete metadata and sparse maintainer information, despite having no detected obfuscation or credential risks.
- Metadata risk is elevated due to sparse maintainer details and missing repository.
- Shell risk is present but likely functional given the package's nature.
Per-check LLM notes
- Network: No network calls detected.
- Shell: Shell execution appears to be querying GPU information, which is likely related to the package's functionality.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The repository is not found and the maintainer's information is sparse, indicating potential risk.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 4.0
Found 2 shell execution pattern(s)
ne try: _PCIE_GEN = int(subprocess.check_output( ["nvidia-smi", "--query-gpu=pcie.link.gen.max", "--try: result = subprocess.check_output( ["nvidia-smi", "--query-gpu=memory.total",
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: uwo.ca>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 3.0
Repository not found (deleted or private)
Repository not found (deleted or private)
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 M3Drop
Your task is to create a fully functional mini-application named 'SingleCellExplorer' that leverages the M3Drop package for analyzing single-cell RNA sequencing data. This application will serve as a user-friendly tool for researchers and biologists to explore their single-cell RNA-seq datasets more effectively. Here are the key steps and features your application should include: 1. **Data Importation**: Allow users to upload their single-cell RNA-seq datasets in common formats such as .csv or .txt. Ensure that the application checks the integrity of the uploaded file and provides feedback if the format is incorrect. 2. **Data Preprocessing**: Implement a preprocessing step where the application cleans the dataset by removing low-quality cells and genes with insufficient expression. Utilize M3Drop's capabilities here to perform quality control measures specific to single-cell RNA-seq data. 3. **Gene Expression Analysis**: Use M3Drop to identify differentially expressed genes between specified cell types or conditions within the dataset. The application should provide visualizations (e.g., heatmaps, volcano plots) to help users understand these differences intuitively. 4. **Cell Type Classification**: Integrate machine learning models trained on known single-cell RNA-seq datasets to classify cells into different types based on gene expression patterns. This classification should leverage M3Drop's methods for feature selection and dimensionality reduction. 5. **User Interface**: Develop a clean, intuitive web-based interface using Flask or Django where users can interact with their data, view results, and export analyses in various formats. 6. **Documentation and Support**: Provide comprehensive documentation detailing how to use SingleCellExplorer, including setup instructions, API references, and FAQs. Additionally, set up a support channel (e.g., email, Slack) where users can get assistance. 7. **Testing and Validation**: Ensure the application is thoroughly tested for accuracy and reliability. Validate its performance against benchmark datasets provided by the M3Drop package authors. By following these steps, you'll create a powerful yet accessible tool for single-cell RNA-seq data analysis, significantly enhancing the research capabilities of biologists and geneticists.