CellProfiler-nightly

v5.0.0.dev643 safe
3.0
Low Risk

CellProfiler is a free open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically.

🤖 AI Analysis

Final verdict: SAFE

The package has low risks associated with network, shell execution, obfuscation, and credential handling. There are no indications of malicious intent or supply-chain attacks.

  • Low network risk
  • Potential risk in shell execution needs monitoring
  • No signs of obfuscation or credential harvesting
Per-check LLM notes
  • Network: The network calls seem to be related to fetching updates or metadata from GitHub, which is common for software packages.
  • Shell: Shell executions may be used for clearing the console and keeping it open, but can also execute arbitrary commands, posing a risk if not properly sanitized.
  • 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 score 3.0

Found 2 network call pattern(s)

  • e.urlencode(params) req = urllib.request.Request(ERROR_URL, data, headers) import wx try:
  • n try: response = requests.get("https://api.github.com/repos/cellprofiler/cellprofiler/rele
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • ings from PyInstaller os.system('cls') # For Windows builds use built-in Java for Ce
  • s." ) os.system("pause") # Keep console window open until keypress.
  • ings from PyInstaller os.system('clear') print(f"Starting CellProfiler {cellprofiler_ver
  • t("/Contents")[0] os.system(f"open -na {app_path}") else: os.system(
  • }") else: os.system("python3 -m cellprofiler") def __on_help_path_list(self
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: broadinstitute.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository CellProfiler/CellProfiler appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Anne Carpenter, Thouis (Ray) Jones, Lee Kamentsky, Vebjorn Ljosa, David Logan, Mark Bray, Madison Swain-Bowden, Allen Goodman, Claire McQuinn, Alice Lucas, Callum Tromans-Coia" 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 CellProfiler-nightly 
Create a mini-application that automates the analysis of biological cell images using the 'CellProfiler-nightly' package. This tool will be aimed at biologists who want to analyze large datasets of cell images without needing extensive programming knowledge. Your application should include the following steps and features:

1. **Image Upload Interface**: Develop a user-friendly interface where users can upload multiple image files (e.g., .jpg, .png). The application should support batch processing of these images.
2. **Preprocessing Options**: Implement options for basic image preprocessing such as resizing, contrast adjustment, and noise reduction. Users should be able to select from predefined settings or customize these parameters.
3. **Cell Segmentation**: Utilize 'CellProfiler-nightly' to perform automatic segmentation of cells within uploaded images. Ensure the application provides visual feedback on how the segmentation is performed, allowing users to adjust parameters if necessary.
4. **Feature Extraction**: Extract key features from segmented cells, such as size, shape, texture, and intensity. These features should be presented in a tabular format alongside corresponding image thumbnails for easy reference.
5. **Data Visualization**: Offer various visualization options for the extracted data, including scatter plots, histograms, and heatmaps. Users should be able to explore relationships between different features.
6. **Report Generation**: Allow users to generate comprehensive reports summarizing their analysis. Reports should include visualizations, statistical summaries, and detailed explanations of the methods used.
7. **Integration with External Tools**: Provide an option to export the analyzed data in formats compatible with other scientific tools like Excel or R for further analysis.
8. **User Documentation and Support**: Include detailed documentation explaining how to use each feature of the application. Also, provide a FAQ section addressing common issues and troubleshooting tips.

This project aims to streamline the process of analyzing biological cell images, making it accessible and efficient for researchers with varying levels of technical expertise.