🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risk indicators with no network or shell execution activities and minimal obfuscation. While there are signs of lower maintenance efforts, these do not point towards any malicious activities.

Low network and shell execution risks
Minimal obfuscation and credential risks
Signs of lower maintenance effort

Per-check LLM notes

Network: No network calls detected, which is normal and expected for most Python packages unless they require external services.
Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands from within the package.
Obfuscation: The detected pattern appears to be a standard method for declaring a namespace in Python packages and does not indicate malicious obfuscation.
Credentials: No patterns indicative of credential harvesting were detected.
Metadata: The package shows some signs of low maintenance effort but does not exhibit clear indicators of malicious intent.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

exinfo_no_detailmenu = False __import__("pkg_resources").declare_namespace(__name__) # orangecontrib is a namespace

✓ 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: biolab.si

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://orange.biolab.si

✓ Git Repository History

Repository biolab/orange3-single-cell appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Bioinformatics Laboratory, FRI UL" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with Orange3-SingleCell

Create a mini-application called 'SingleCellExplorer' using Python and the 'Orange3-SingleCell' package to analyze and visualize single-cell RNA sequencing (scRNA-seq) data. Your application should provide users with an intuitive interface to upload their scRNA-seq datasets, perform common bioinformatics analyses, and generate visualizations directly within the app. Here’s a detailed breakdown of what your application should include:

1. **Data Import**: Allow users to upload their own scRNA-seq dataset in standard formats like `.h5ad`, `.loom`, or `.txt`. Ensure that the application checks the integrity of the uploaded file before proceeding.

2. **Basic Data Preprocessing**: Implement basic preprocessing steps such as filtering cells based on gene expression thresholds, normalizing the data, and performing dimensionality reduction techniques (e.g., PCA).

3. **Clustering Analysis**: Use the 'Orange3-SingleCell' package to perform clustering analysis on the preprocessed data. Visualize the results in a scatter plot where each point represents a cell and its color represents the cluster it belongs to.

4. **Gene Expression Visualization**: Allow users to select specific genes and visualize their expression levels across different clusters. This could be done through heatmaps or violin plots.

5. **Interactive Exploration**: Enable interactive exploration of the data, such as hovering over points in the scatter plot to see detailed information about individual cells, including their gene expression profiles.

6. **Report Generation**: Integrate a feature that allows users to generate a report summarizing the analysis performed, including key findings from the clustering and gene expression analysis. The report should be exportable as a PDF document.

7. **User Interface Design**: Focus on creating a clean, user-friendly interface that guides users through each step of the analysis process. Include tooltips and help sections to assist users who might not be familiar with bioinformatics concepts.

8. **Documentation and Help**: Provide comprehensive documentation for both users and developers, explaining how to use the application effectively and how to extend it with additional functionality.

The 'Orange3-SingleCell' package will be utilized primarily for its advanced functionalities in single-cell data analysis, particularly in clustering algorithms and visualization tools. It will also serve as the backbone for integrating various bioinformatics workflows into a cohesive application.