asari-metabolomics

v1.17.1 safe
4.0
Medium Risk

metabolomics data processing

🤖 AI Analysis

Final verdict: SAFE

The package has a low risk score with no detected obfuscation or credential risks. While there are potential risks associated with network calls and executing shell commands, these are common practices and do not necessarily indicate malicious intent.

  • Low obfuscation and credential risks
  • Potential risks from network calls and shell commands
Per-check LLM notes
  • Network: The package makes network calls to download files which is typical for packages requiring external resources.
  • Shell: Executing shell commands could be risky if not properly sanitized or controlled, potentially allowing for unintended operations.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has only one package on PyPI, which may indicate a new or less active account.

📦 Package Quality Overall: Medium (5.4/10)

✦ High Test Suite 9.0

Test suite present — 6 test file(s) found

  • 6 test file(s) detected (e.g. test.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (17296 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in shuzhao-li/asari
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • response = requests.get(converter_release_url) with open(local_z
  • d the ZIP file response = requests.get(url, stream=True) response.raise_for_status() # Ext
  • directory.""" response = requests.get(url, stream=True) response.raise_for_status() # Raise a
  • in datasets: r = requests.get(dataset) if dataset.endswith(".zip"):
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • try: subprocess.run(engine + [converter_path], capture_output=True)
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository shuzhao-li/asari appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Shuzhao Li" 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 asari-metabolomics 
Create a mini-application named 'MetaboliteAnalyzer' using Python and the 'asari-metabolomics' package. This application will serve as a tool for researchers and scientists to process and analyze metabolomics data efficiently. The goal of 'MetaboliteAnalyzer' is to simplify the workflow from raw data import to advanced analysis, providing insights into metabolic profiles.

### Key Features:
1. **Data Import**: Users should be able to upload raw metabolomics data in common formats such as CSV or Excel. The application must validate the imported data to ensure it meets the necessary standards for processing.
2. **Preprocessing**: Implement basic preprocessing steps such as normalization, missing value imputation, and noise reduction using functionalities provided by 'asari-metabolomics'.
3. **Feature Selection**: Integrate feature selection techniques to identify significant metabolites contributing to observed differences between groups. This could involve statistical tests or machine learning-based methods.
4. **Visualization**: Provide interactive visualizations of the data, including scatter plots, heatmaps, and PCA (Principal Component Analysis) plots, to help users understand the structure and patterns within their data.
5. **Analysis Reports**: Generate comprehensive reports summarizing the key findings from the analysis, including statistical significance, identified biomarkers, and overall trends.

### Utilization of 'asari-metabolomics':
- Use 'asari-metabolomics' for its specialized functions related to metabolomics data processing. This includes but is not limited to, handling large datasets, performing normalization, conducting quality control checks, and applying advanced analytical techniques specific to metabolomics research.
- Leverage the package's capabilities in feature extraction and selection to enhance the accuracy and relevance of the analysis performed by 'MetaboliteAnalyzer'.
- Ensure that the application integrates seamlessly with 'asari-metabolomics', allowing for a streamlined user experience where complex operations are abstracted away, presenting only the necessary inputs and outputs to the user.

This project aims to bridge the gap between raw data and meaningful biological insights, making advanced metabolomics analysis accessible to a broader audience.

💬 Discussion Feed

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