🤖 AI Analysis

Final verdict: SAFE

The package does not exhibit significant risks based on the provided analysis notes. While there is some obfuscation that merits further investigation, the lack of network calls, shell executions, and credential harvesting suggests benign intent.

Moderate obfuscation risk
No network or shell execution detected
No credential risk

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires internet access to function.
Shell: No shell execution detected, indicating no direct command-line interface manipulation attempts.
Obfuscation: The code snippet appears to be obfuscated, but it's not clear if this is for malicious purposes or legitimate encoding/encryption.
Credentials: No credentials or secrets harvesting patterns were detected.
Metadata: The package shows some signs of being new or less active, but there are no clear red flags.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

ath(file_path) model.eval() if compression: with gzip.open(file_pa

✓ 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: xmu.edu.cn

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Feng Zeng" 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 SURE-tools

Create a mini-application called 'CellExplorer' using the Python package 'SURE-tools', which is designed for the Succinct Representation of Single Cells. This application will allow researchers to explore and analyze single-cell data efficiently. The main goal is to provide a user-friendly interface where users can upload their single-cell RNA sequencing data, visualize it, and perform basic clustering and differential expression analysis. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Start by setting up your Python environment. Install necessary packages including 'SURE-tools', 'pandas', 'numpy', 'matplotlib', and 'seaborn'. Ensure you have the latest version of 'SURE-tools' to access all its functionalities.

2. **Data Upload & Preprocessing**: Implement a feature where users can upload their single-cell RNA sequencing data in standard formats like .h5ad or .csv. Use 'SURE-tools' to preprocess the data, ensuring normalization and log transformation if necessary. Display a progress bar and any relevant statistics about the dataset upon completion.

3. **Visualization**: Develop interactive visualizations of the data using 'SURE-tools'. Create scatter plots based on principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to help users understand the structure of their data. Allow users to customize these plots with different color schemes based on sample metadata.

4. **Clustering Analysis**: Integrate 'SURE-tools' to perform clustering analysis on the preprocessed data. Provide options for various clustering algorithms supported by 'SURE-tools', such as K-means or Louvain method. Display the results through interactive heatmaps and cluster dendrograms.

5. **Differential Expression Analysis**: Utilize 'SURE-tools' to conduct differential expression analysis between identified clusters. Generate volcano plots and heatmaps showing significant genes differentially expressed between clusters. Enable users to download the results in .csv format for further analysis.

6. **User Interface**: Design a clean and intuitive user interface using a web framework like Streamlit or Flask. Ensure the UI is responsive and easy to navigate, providing clear instructions and tooltips for each section of the app.

7. **Documentation & Deployment**: Write comprehensive documentation explaining how to use 'CellExplorer', including installation instructions and examples of input files. Deploy the application on a cloud platform like Heroku or AWS, making it accessible to a broader audience of researchers.

By following these steps, 'CellExplorer' will become a valuable tool for researchers working with single-cell RNA sequencing data, leveraging the powerful functionalities of 'SURE-tools' to streamline their analysis process.