AI Analysis
Final verdict: SAFE
The SC-Framework v0.15.1 package presents minimal risk based on the analysis notes provided. All checks indicate low risk with no network calls, shell executions, obfuscation, or credential harvesting detected.
- No network calls detected
- No shell execution detected
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communications.
- Shell: No shell execution detected, indicating no immediate risk of unauthorized command execution.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: No red flags detected in the metadata.
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: mpi-bn.mpg.de>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository loosolab/SC-Framework appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Hendrik Schultheis, Jan Detleffsen, René Wiegandt, Mette Bentsen, Yousef Alayoubi, Guilherme Valente, Micha Frederick Keßler, Brenton Joey Bruns, Dlnija Mirza, Angeline Usanayo, Jasmin Walter, Philipp Goymann, Moritz Hobein, Carsten Kuenne, Mario Looso" 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 SC-Framework
Create a user-friendly web application using Python's Flask framework that integrates the 'SC-Framework' package for single-cell RNA sequencing data analysis. This application will allow researchers to upload their single-cell RNA-seq datasets, perform basic preprocessing steps, and visualize the results interactively. Here’s a detailed breakdown of the application’s functionalities and how 'SC-Framework' will be utilized: 1. **Data Upload**: Users should be able to upload their single-cell RNA-seq datasets in standard formats such as .h5ad or .loom. 2. **Preprocessing**: Implement functions within the application to preprocess the data using 'SC-Framework'. This includes normalization, log-transformation, and batch-effect correction if applicable. 3. **Dimensionality Reduction**: Use 'SC-Framework' to perform dimensionality reduction techniques like PCA and t-SNE on the preprocessed data. 4. **Clustering Analysis**: Apply clustering algorithms available in 'SC-Framework' to identify cell clusters based on gene expression patterns. 5. **Gene Expression Visualization**: Visualize gene expression levels across different cell clusters using heatmaps and volcano plots. 6. **Interactive Dashboard**: Develop an interactive dashboard where users can explore the clustering results through clickable plots, allowing them to select specific cells or genes for more detailed analysis. 7. **Report Generation**: Enable users to generate and download comprehensive reports summarizing their analysis, including visualizations and key statistics. The application should be designed to handle large datasets efficiently and provide clear, informative error messages when necessary. Additionally, ensure that the UI is intuitive and accessible to non-technical users.