childhealthbiostatscore/CHCO-Code
Sep 2025 – Sep 2025
1 Month active
Languages Used
MarkdownPythonR
Technical Skills
BioinformaticsData subsettingPseudotime analysisPython programmingR programmingSingle-cell RNA sequencing
Ye Ji Choi
PROFILE
Ye Ji Choi
Yejin Choi refactored and enhanced the single-cell RNA sequencing analysis workflow in the childhealthbiostatscore/CHCO-Code repository, focusing on improving pseudotime analysis and enabling cohort-specific data subsetting. Using R and Python, Yejin updated environment paths to ensure reproducibility and introduced new R Markdown files to support data exploration and reporting. The technical approach emphasized modular code structure and reproducible research practices, allowing for more granular cohort insights and streamlined downstream validation. This work deepened the analytical capabilities of the workflow, addressed the need for flexible cohort analysis, and produced reporting-ready artifacts for collaborative bioinformatics research and validation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
627
Activity Months1
Your Network
48 peopleSame Organization
@n3440.hyak.local1
Work History
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 1) Key features delivered: Refactor and enhance single-cell RNA sequencing analysis workflow in CHCO-Code, including pseudotime improvements and cohort-specific data subsetting; update Python environment paths; add new R Markdown files for data exploration and analysis. 2) Major bugs fixed: None reported this month. 3) Overall impact and accomplishments: Improved analysis reproducibility and efficiency; enabled more granular cohort insights; prepared reporting-ready artifacts for downstream validation and collaboration. 4) Technologies/skills demonstrated: R, Python environment management, pseudotime analysis, data subsetting, R Markdown, and Git version control.
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 1) Key features delivered: Refactor and enhance single-cell RNA sequencing analysis workflow in CHCO-Code, including pseudotime improvements and cohort-specific data subsetting; update Python environment paths; add new R Markdown files for data exploration and analysis. 2) Major bugs fixed: None reported this month. 3) Overall impact and accomplishments: Improved analysis reproducibility and efficiency; enabled more granular cohort insights; prepared reporting-ready artifacts for downstream validation and collaboration. 4) Technologies/skills demonstrated: R, Python environment management, pseudotime analysis, data subsetting, R Markdown, and Git version control.
September 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonR
Technical Skills
BioinformaticsData subsettingPseudotime analysisPython programmingR programmingSingle-cell RNA sequencing
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