atlas-smilies

v1.0.0 safe
4.0
Medium Risk

A multi-omics single cell trajectory inference framework

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity based on its current analysis. However, due to its lack of community engagement and recent introduction, further monitoring is advised.

  • No network calls or shell executions detected.
  • Metadata risk noted due to the package's novelty and limited community involvement.
Per-check LLM notes
  • Network: No network calls detected, which is normal for a package named 'atlas-smilies' that likely handles emoticons or similar graphical elements without external dependencies.
  • Shell: No shell execution patterns detected, aligning with expectations for a benign utility focused on managing or displaying smilies.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is new and lacks community engagement, which raises some suspicion but does not conclusively indicate malice.

📦 Package Quality Overall: Medium (6.0/10)

✦ High Test Suite 9.0

Test suite present — 21 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 21 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://atlas-smilies.readthedocs.io/
  • 2 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3559 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

  • 40 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 38 commits in smilies-polito/atlas-smilies
  • Single author but highly active (38 commits)

🔬 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: polito.it>

✓ 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 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "smilies-polito" 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 atlas-smilies 
Create a mini-application that leverages the 'atlas-smilies' package to visualize and analyze single-cell trajectory data from multi-omics datasets. This application should allow users to upload their own multi-omics data (RNA-seq, ATAC-seq, etc.), preprocess it using 'atlas-smilies', infer trajectories of cellular development, and visualize these trajectories in a user-friendly interface. Here are the steps and features your application should include:

1. **Data Upload Interface**: Develop a web-based or command-line interface where users can upload their multi-omics datasets. Ensure the interface supports common file formats such as CSV, TSV, or specialized single-cell sequencing formats.
2. **Preprocessing**: Use 'atlas-smilies' to preprocess the uploaded data. This includes normalization, imputation, and integration of different omics layers. Provide options for users to customize preprocessing parameters if possible.
3. **Trajectory Inference**: Implement functionality within the application to use 'atlas-smilies' for inferring developmental trajectories from the preprocessed data. The application should automatically detect potential starting points and endpoints of cellular trajectories based on the data.
4. **Visualization**: Create an interactive visualization component where users can explore the inferred trajectories. Features could include zooming, panning, highlighting specific cells or genes, and exporting visualizations as images or videos.
5. **Annotation and Analysis**: Allow users to annotate cells or genes on the trajectory map. Additionally, provide basic analysis tools like differential expression analysis along the trajectory or clustering analysis.
6. **Report Generation**: Integrate a feature that generates a report summarizing the analysis performed, including key findings, statistical results, and visual representations of the trajectories.
7. **Documentation and Support**: Ensure comprehensive documentation is available for both developers and end-users. Include FAQs, tutorials, and contact information for support.

By building this application, you will create a powerful tool for researchers and biologists to better understand complex cellular processes through the lens of multi-omics data analysis.

💬 Discussion Feed

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