childhealthbiostatscore/CHCO-Code
Apr 2025 – Apr 2025
1 Month active
Languages Used
PythonR
Technical Skills
BioinformaticsCloud Storage IntegrationData AnalysisData preprocessingData visualizationHigh-Performance Computing
Hailey Hampson
PROFILE
Hailey Hampson
During April 2025, Hannah Hampson developed an end-to-end kidney single-cell RNA sequencing analysis workflow for the childhealthbiostatscore/CHCO-Code repository. She designed an R Markdown notebook that integrates S3-based data loading, initial quality control, and preprocessing scaffolding, establishing a foundation for scalable and reproducible analysis on the Hyak high-performance computing cluster. Her work included updating the environment to support JAGS, jsonlite, reticulate, boto3, and pandas, enabling cross-language workflows in both R and Python. This setup allows for efficient experimentation and future extension to comparative studies, providing a robust framework for collaborative bioinformatics research and data-driven insights.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
752
Activity Months1
Work History
April 2025
3 Commits • 1 Features
Apr 1, 2025Month: 2025-04 — Key outcomes: Delivered an end-to-end Kidney scRNA analysis workflow on Hyak with an initial R Markdown notebook, S3 data loading, QC/preprocessing scaffolding, and a framework for downstream analyses; Updated environment and dependencies to enable JAGS, jsonlite/reticulate, boto3, and pandas; Established reproducible setup for cross-language workflows and HPC-based analyses. This work provides foundation for scalable, reproducible kidney single-cell analysis pipelines on HPC, enabling faster experimentation and broader collaboration. Commit traceability provided via recent commits.
3 Commits • 1 Features
Apr 1, 2025Month: 2025-04 — Key outcomes: Delivered an end-to-end Kidney scRNA analysis workflow on Hyak with an initial R Markdown notebook, S3 data loading, QC/preprocessing scaffolding, and a framework for downstream analyses; Updated environment and dependencies to enable JAGS, jsonlite/reticulate, boto3, and pandas; Established reproducible setup for cross-language workflows and HPC-based analyses. This work provides foundation for scalable, reproducible kidney single-cell analysis pipelines on HPC, enabling faster experimentation and broader collaboration. Commit traceability provided via recent commits.
April 2025
Activity
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Quality Metrics
Correctness73.4%
Maintainability73.4%
Architecture73.4%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonR
Technical Skills
BioinformaticsCloud Storage IntegrationData AnalysisData preprocessingData visualizationHigh-Performance ComputingPackage ManagementPythonPython programmingRR DevelopmentR programmingSingle-cell RNA sequencing analysisStatistical modeling
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