🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across all critical areas, with only metadata suggesting it might be new or poorly maintained. There is no concrete evidence of malicious activity.

Low risk in network, shell, and obfuscation checks.
Metadata suggests new or poorly maintained status.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell executions detected, indicating no immediate risk of command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting secure handling of secrets.
Metadata: The package shows signs of being new or poorly maintained, but there's no clear evidence of malicious intent.

🔬 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: scilifelab.se>

✓ 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

Author name is missing or very short
Author "" 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 Cell-GPS

Create a mini-application that leverages the 'Cell-GPS' package to analyze and visualize spatial omics data from single-cell RNA sequencing experiments. This application should allow researchers to upload their own spatial transcriptomics datasets, perform various analyses using Cell-GPS functionalities, and generate interactive visualizations of the results.

Steps:
1. Set up a user-friendly interface where users can upload their spatial transcriptomics dataset files (e.g., .h5ad format).
2. Implement data preprocessing steps using Cell-GPS to ensure the dataset is ready for analysis. This includes normalization, filtering, and quality control checks.
3. Allow users to select specific cell types or gene sets for focused analysis. Utilize Cell-GPS to identify spatially variable genes and map them onto the tissue space.
4. Integrate Cell-GPS clustering methods to group cells based on their gene expression profiles within the spatial context. Display these clusters as distinct regions on an interactive tissue map.
5. Offer advanced visualization tools, such as heatmaps and scatter plots, which highlight the spatial distribution of selected genes or cell types across different clusters.
6. Enable users to save and export their analysis results in multiple formats, including image files and downloadable reports.

Features:
- User authentication for saving personal datasets and results.
- Real-time progress tracking during analysis.
- Comparison tools to contrast results between different datasets.
- Integration with external databases for additional annotation information.
- Educational resources explaining key concepts in spatial omics analysis.

Utilization of Cell-GPS:
Throughout the development process, you will heavily rely on Cell-GPS for its specialized functions in handling spatial omics data. From preprocessing through to clustering and visualization, Cell-GPS provides the necessary algorithms and pipelines to conduct comprehensive spatial analysis.