childhealthbiostatscore/CHCO-Code
Oct 2024 – Oct 2025
5 Months active
Languages Used
MarkdownRR MarkdownShellPythonSQL
Technical Skills
BioinformaticsBiostatisticsData AnalysisData CleaningData VisualizationData Wrangling
Hailey
PROFILE
Hailey
Heather Hampson developed and maintained advanced bioinformatics workflows in the childhealthbiostatscore/CHCO-Code repository, focusing on multi-omics integration, single-cell RNA sequencing, and clinical variable analysis for liver and kidney disease research. She engineered robust data pipelines using R and Python, leveraging AWS S3 for scalable cloud storage and reproducible analytics. Her work included implementing NEBULA-based differential expression, DESeq2 workflows, and custom visualization tools to support cross-study biomarker discovery and exposure-risk assessment. By standardizing data formatting, automating reporting, and enhancing deployment reliability, Heather delivered analysis-ready datasets and reproducible results, demonstrating strong depth in statistical modeling, data wrangling, and cloud integration.
Overall Statistics
Feature vs Bugs
85%Features
Repository Contributions
77Total
Bugs
5
Commits
77
Features
29
Lines of code
109,519
Activity Months5
Work History
October 2025
8 Commits • 7 Features
Oct 1, 2025Month 2025-10: Delivered a focused package of feature enhancements and maintenance in CHCO-Code to advance liver disease and adipose tissue analyses while reducing technical debt. Notable outcomes include removing deprecated R code for GSEA and CellChat and standardizing filenames to improve stability; introducing triglyceride-focused liver analysis by treating TGs as the sole clinical variable and updating clinical_variables; enabling Nebula-based differential expression across liver cell types with new metadata and generation of volcano plots and heatmaps; implementing liver zonation scoring with zone markers and associated visualizations; adding Table 1 generation in HTML/CSV with OneDrive saving; and enhancing cell count proportions visuals with reticulate integration and custom cell type orders. In parallel, VAT/SAT differential expression was refactored to run with DESeq2, loading data from S3 and producing volcano plots, along with cleanup of unused code.
8 Commits • 7 Features
Oct 1, 2025Month 2025-10: Delivered a focused package of feature enhancements and maintenance in CHCO-Code to advance liver disease and adipose tissue analyses while reducing technical debt. Notable outcomes include removing deprecated R code for GSEA and CellChat and standardizing filenames to improve stability; introducing triglyceride-focused liver analysis by treating TGs as the sole clinical variable and updating clinical_variables; enabling Nebula-based differential expression across liver cell types with new metadata and generation of volcano plots and heatmaps; implementing liver zonation scoring with zone markers and associated visualizations; adding Table 1 generation in HTML/CSV with OneDrive saving; and enhancing cell count proportions visuals with reticulate integration and custom cell type orders. In parallel, VAT/SAT differential expression was refactored to run with DESeq2, loading data from S3 and producing volcano plots, along with cleanup of unused code.
October 2025
September 2025
26 Commits • 10 Features
Sep 1, 2025September 2025 monthly summary for CHCO-Code: Delivered deployment and data-pipeline enhancements focused on reliability, reproducibility, and faster research cycles. Key features delivered include Lambda versioning across liver code, libraries, and dependencies with updated packaging; a local-only liver code version for safe offline testing; and ForHyak deployment updates to ensure environment parity. Strengthened data pipelines with Kopah metadata loading, path updates, and prep for GSEA; and hepatocyte/liver gene expression work with clinical vars integration and finalization of hepatocyte DEG modeling.
September 2025
26 Commits • 10 Features
Sep 1, 2025September 2025 monthly summary for CHCO-Code: Delivered deployment and data-pipeline enhancements focused on reliability, reproducibility, and faster research cycles. Key features delivered include Lambda versioning across liver code, libraries, and dependencies with updated packaging; a local-only liver code version for safe offline testing; and ForHyak deployment updates to ensure environment parity. Strengthened data pipelines with Kopah metadata loading, path updates, and prep for GSEA; and hepatocyte/liver gene expression work with clinical vars integration and finalization of hepatocyte DEG modeling.
July 2025
2 Commits • 1 Features
Jul 1, 20252025-07 Monthly Summary for childhealthbiostatscore/CHCO-Code focusing on data access reliability, analytic workflow improvements, and performance. Delivered a NEBULA-based differential expression analysis workflow for liver scRNA-seq (steatosis) with visualization, and resolved critical data access and dependency issues to enable reproducible, scalable analyses.
2 Commits • 1 Features
Jul 1, 20252025-07 Monthly Summary for childhealthbiostatscore/CHCO-Code focusing on data access reliability, analytic workflow improvements, and performance. Delivered a NEBULA-based differential expression analysis workflow for liver scRNA-seq (steatosis) with visualization, and resolved critical data access and dependency issues to enable reproducible, scalable analyses.
July 2025
November 2024
29 Commits • 7 Features
Nov 1, 2024November 2024 monthly summary for CHCO-Code: Key features delivered and improvements: - Dataset formatting, analysis preparation, and reporting improvements: revamped dataset formatting for analysis, updated tables and descriptive reporting, enabling analysis-ready data and more accurate outputs (commits include aafdf90, 9474d29, 88544396, 620f10e9, 823eccb3). - Local reporting scripts and project directory setup: simplified local table-generation scripts and standardized project directories to improve reproducibility and onboarding (commits bdcf80de, b9dc0ada). - Multiomics scripting and xtune enhancements: added multiomics scripting, metabolomics code, and xtune-related functionality with ongoing refinements (commits ca0f2559, f4c4eb9d, 82ac588c, f08fdf99). - Memory sizing and covariate integration: updated memory sizing logic and integrated covariates into analyses to enhance model accuracy and comparability (commits aaa45990, 986c56b7). - Data filtering by type and cohort segmentation: introduced type-based filtering to refine data subsets for targeted analyses (commit d149a010). - Data formatting and quality control: prepared genes for IPA formatting and implemented subset QC to improve data quality checks (commits fb341c1f, 2cb85b44). Major bugs fixed: - Removed results saved on GitHub to prevent unintended data exposure and privacy risk (commit d79cbef6). - Data cleanup and housekeeping: removed stray artifacts and accidentally saved files to GitHub to reduce repository noise and potential confusion (commits 8b001bc0, 7f37bf8d, 85fac434). - File path and local configuration fixes: standardized and corrected file paths for local environments to ensure reproducible runs (commits b62c2f20, 03484f2f). Overall impact and accomplishments: - Delivered a robust, reproducible analytics pipeline with richer, analysis-ready data and improved reporting capabilities, enabling faster decision support and higher confidence in findings. - Strengthened data governance and privacy by removing sensitive results from public/remote storage and improving artifact cleanup, reducing risk and audit exposure. - Enabled more accurate cross-cohort analyses through covariate integration, improved memory sizing, and refined data filtering, supporting scalable, insightful insights across projects. Technologies/skills demonstrated: - Advanced data wrangling and formatting for analysis and reporting; scripting and automation of local report generation. - Multiomics pipeline development including metabolomics and xtune features; modular code organization for extensibility. - Covariate modeling and robust statistical preparation (liver AST results, hurdle models, participant aggregation) and model summarization. - Quality control, reproducibility best practices, and configuration management (local paths, directory structure).
November 2024
29 Commits • 7 Features
Nov 1, 2024November 2024 monthly summary for CHCO-Code: Key features delivered and improvements: - Dataset formatting, analysis preparation, and reporting improvements: revamped dataset formatting for analysis, updated tables and descriptive reporting, enabling analysis-ready data and more accurate outputs (commits include aafdf90, 9474d29, 88544396, 620f10e9, 823eccb3). - Local reporting scripts and project directory setup: simplified local table-generation scripts and standardized project directories to improve reproducibility and onboarding (commits bdcf80de, b9dc0ada). - Multiomics scripting and xtune enhancements: added multiomics scripting, metabolomics code, and xtune-related functionality with ongoing refinements (commits ca0f2559, f4c4eb9d, 82ac588c, f08fdf99). - Memory sizing and covariate integration: updated memory sizing logic and integrated covariates into analyses to enhance model accuracy and comparability (commits aaa45990, 986c56b7). - Data filtering by type and cohort segmentation: introduced type-based filtering to refine data subsets for targeted analyses (commit d149a010). - Data formatting and quality control: prepared genes for IPA formatting and implemented subset QC to improve data quality checks (commits fb341c1f, 2cb85b44). Major bugs fixed: - Removed results saved on GitHub to prevent unintended data exposure and privacy risk (commit d79cbef6). - Data cleanup and housekeeping: removed stray artifacts and accidentally saved files to GitHub to reduce repository noise and potential confusion (commits 8b001bc0, 7f37bf8d, 85fac434). - File path and local configuration fixes: standardized and corrected file paths for local environments to ensure reproducible runs (commits b62c2f20, 03484f2f). Overall impact and accomplishments: - Delivered a robust, reproducible analytics pipeline with richer, analysis-ready data and improved reporting capabilities, enabling faster decision support and higher confidence in findings. - Strengthened data governance and privacy by removing sensitive results from public/remote storage and improving artifact cleanup, reducing risk and audit exposure. - Enabled more accurate cross-cohort analyses through covariate integration, improved memory sizing, and refined data filtering, supporting scalable, insightful insights across projects. Technologies/skills demonstrated: - Advanced data wrangling and formatting for analysis and reporting; scripting and automation of local report generation. - Multiomics pipeline development including metabolomics and xtune features; modular code organization for extensibility. - Covariate modeling and robust statistical preparation (liver AST results, hurdle models, participant aggregation) and model summarization. - Quality control, reproducibility best practices, and configuration management (local paths, directory structure).
October 2024
12 Commits • 4 Features
Oct 1, 2024October 2024 — CHCO-Code monthly highlights: four major feature deliverables spanning PAH data integration for OAT analysis, scRNA-seq enhancements, a brain biomarkers multi-omics integration framework, and kidney scRNA-seq analysis with enhanced statistics. These workstreams delivered standardized, cross-study data, richer analytics, and new visualization capabilities, driving improved exposure-risk assessments and biomarker research. No major bugs reported; notable stability gains came from package updates and improved statistical tooling. Key business impact includes faster turnarounds for cross-study analyses, improved data quality, and clearer biomarker relationships.
12 Commits • 4 Features
Oct 1, 2024October 2024 — CHCO-Code monthly highlights: four major feature deliverables spanning PAH data integration for OAT analysis, scRNA-seq enhancements, a brain biomarkers multi-omics integration framework, and kidney scRNA-seq analysis with enhanced statistics. These workstreams delivered standardized, cross-study data, richer analytics, and new visualization capabilities, driving improved exposure-risk assessments and biomarker research. No major bugs reported; notable stability gains came from package updates and improved statistical tooling. Key business impact includes faster turnarounds for cross-study analyses, improved data quality, and clearer biomarker relationships.
October 2024
Activity
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Quality Metrics
Correctness83.6%
Maintainability82.6%
Architecture77.4%
Performance72.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonRR MarkdownSQLShell
Technical Skills
AWS S3BioinformaticsBiostatisticsCell-Cell Communication AnalysisCloud StorageCloud Storage IntegrationCloud Storage Integration (S3)Cloud computing (AWS S3, Hyak)Data AnalysisData CleaningData LoadingData PreprocessingData VisualizationData WranglingData preprocessing
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