Yeji Choi developed and maintained end-to-end data analysis and integration pipelines for the CHCO-Code repository, supporting multi-study clinical and omics research. She engineered scalable workflows for single-cell RNA sequencing, proteomics, and imaging data, emphasizing robust data harmonization, reproducible reporting, and cross-study comparability. Using R and Python, Yeji implemented advanced statistical modeling, visualization, and data cleaning routines, integrating AWS S3 for cloud storage and REDCap for clinical data extraction. Her work enabled automated reporting, improved data quality, and streamlined analytics, delivering actionable insights for clinical decision-making. The depth of her engineering ensured reliable, maintainable, and extensible research infrastructure.
Monthly Summary for 2026-04: Delivered a BMI/DXA-oriented comparative analysis feature for Type 1 Diabetes with IMPROVE imaging integration in CHCO-Code. Added normal weight vs overweight/obese analyses and refined study-affiliation categorization, improving readability and organization of analysis results. This work enables more precise body-composition insights and scalable research for ongoing IMPROVE and Type 1 Diabetes studies.
Monthly Summary for 2026-04: Delivered a BMI/DXA-oriented comparative analysis feature for Type 1 Diabetes with IMPROVE imaging integration in CHCO-Code. Added normal weight vs overweight/obese analyses and refined study-affiliation categorization, improving readability and organization of analysis results. This work enables more precise body-composition insights and scalable research for ongoing IMPROVE and Type 1 Diabetes studies.
In March 2026, the CHCO-Code team delivered a focused set of analytics enhancements across simulation workflows, data visualization, and demographic modeling, delivering tangible business value for research pipelines and decision support. Key features include substantial improvements to the simulation analysis workflow, expanded visualization capabilities for baseline GLP analyses, and data-reporting enhancements with age/sex-adjusted outputs. Core modeling refinements improve data integrity and interpretability, while reliability improvements increase pipeline robustness for downstream analyses.
In March 2026, the CHCO-Code team delivered a focused set of analytics enhancements across simulation workflows, data visualization, and demographic modeling, delivering tangible business value for research pipelines and decision support. Key features include substantial improvements to the simulation analysis workflow, expanded visualization capabilities for baseline GLP analyses, and data-reporting enhancements with age/sex-adjusted outputs. Core modeling refinements improve data integrity and interpretability, while reliability improvements increase pipeline robustness for downstream analyses.
Feb 2026 monthly deliverables for the CHCO-Code repository focused on robust data harmonization, MRI data integration, and scalable analytics for downstream health studies. Implemented data cleaning, improved record identification, and BMI calculations to support analyses; added scRNA-seq analysis capabilities (reversal scores, simulation framework) and enhanced PANTHER data processing and visualization; created an R Markdown report for harmonized health study data. The work improves data quality, reproducibility, and decision-support capabilities across CHCO data pipelines.
Feb 2026 monthly deliverables for the CHCO-Code repository focused on robust data harmonization, MRI data integration, and scalable analytics for downstream health studies. Implemented data cleaning, improved record identification, and BMI calculations to support analyses; added scRNA-seq analysis capabilities (reversal scores, simulation framework) and enhanced PANTHER data processing and visualization; created an R Markdown report for harmonized health study data. The work improves data quality, reproducibility, and decision-support capabilities across CHCO data pipelines.
January 2026: Delivered substantial enhancements to CHCO-Code pipelines and datasets, expanding analytical capabilities, improving data quality and processing reliability, and producing publication-ready visuals. Achieved broader metabolomics coverage, richer GSEA reporting, and robust data workflows supporting ongoing studies in diabetes, renal health, and multi-omics integration.
January 2026: Delivered substantial enhancements to CHCO-Code pipelines and datasets, expanding analytical capabilities, improving data quality and processing reliability, and producing publication-ready visuals. Achieved broader metabolomics coverage, richer GSEA reporting, and robust data workflows supporting ongoing studies in diabetes, renal health, and multi-omics integration.
December 2025 monthly summary for CHCO-Code (childhealthbiostatscore). Focused on delivering three core features with cross-study data integration, closing data gaps, and strengthening pipelines to support biomarker discovery and clinical research partnerships. Business value centers on improved data quality, faster insights, and reproducible workflows across multi-study cohorts.
December 2025 monthly summary for CHCO-Code (childhealthbiostatscore). Focused on delivering three core features with cross-study data integration, closing data gaps, and strengthening pipelines to support biomarker discovery and clinical research partnerships. Business value centers on improved data quality, faster insights, and reproducible workflows across multi-study cohorts.
November 2025 CHCO-Code monthly summary focused on delivering business value through enhanced data analytics, improved reporting, and stronger data organization. Key outcomes include robust single-cell RNA sequencing analysis and visualization improvements for kidney disease, enhanced demographic data processing for clearer study reporting, and refined renal data organization and transcriptomics prioritization to accelerate insight generation. Minor maintenance activities improved repository hygiene (gitignore updates). No major bugs were reported this month; ongoing QA ensures robustness of pipelines powering clinical endpoints interpretation and treatment-effect analyses.
November 2025 CHCO-Code monthly summary focused on delivering business value through enhanced data analytics, improved reporting, and stronger data organization. Key outcomes include robust single-cell RNA sequencing analysis and visualization improvements for kidney disease, enhanced demographic data processing for clearer study reporting, and refined renal data organization and transcriptomics prioritization to accelerate insight generation. Minor maintenance activities improved repository hygiene (gitignore updates). No major bugs were reported this month; ongoing QA ensures robustness of pipelines powering clinical endpoints interpretation and treatment-effect analyses.
October 2025 (CHCO-Code): Delivered feature-rich improvements across ATTEMPT data processing/visualization, RH/RH2 and DKD data cleaning and presentation, KPMP glue grant metrics aggregation, and system logging/maintenance planning. These changes enhanced cross-study data coherence, automated metric reporting, and incident responsiveness, strengthening data-driven decision making for clinicians and researchers.
October 2025 (CHCO-Code): Delivered feature-rich improvements across ATTEMPT data processing/visualization, RH/RH2 and DKD data cleaning and presentation, KPMP glue grant metrics aggregation, and system logging/maintenance planning. These changes enhanced cross-study data coherence, automated metric reporting, and incident responsiveness, strengthening data-driven decision making for clinicians and researchers.
September 2025 CHCO-Code monthly summary: The month delivered substantial improvements in reporting, data integrity, and visualization, driving clearer clinical insights and faster data-driven decisions. Key work focused on expanding core outputs (medulla r2* in main report), improving figure caption quality, enabling external data pulls, and enhancing visualization readability, while establishing scalable documentation through demographics reporting. Robust data quality controls were implemented to reduce inaccuracies, and infrastructure readiness was advanced for Hyak HPC usage to support repeatable runs.
September 2025 CHCO-Code monthly summary: The month delivered substantial improvements in reporting, data integrity, and visualization, driving clearer clinical insights and faster data-driven decisions. Key work focused on expanding core outputs (medulla r2* in main report), improving figure caption quality, enabling external data pulls, and enhancing visualization readability, while establishing scalable documentation through demographics reporting. Robust data quality controls were implemented to reduce inaccuracies, and infrastructure readiness was advanced for Hyak HPC usage to support repeatable runs.
Month 2025-08 focused on delivering a robust, end-to-end data-to-report pipeline in CHCO-Code, with emphasis on data harmonization, multi-omics integration, and clearer visualization/reporting. The month delivered substantive features, major fixes, and scalable tooling that improve report accuracy, reproducibility, and decision support for clinical and research teams.
Month 2025-08 focused on delivering a robust, end-to-end data-to-report pipeline in CHCO-Code, with emphasis on data harmonization, multi-omics integration, and clearer visualization/reporting. The month delivered substantive features, major fixes, and scalable tooling that improve report accuracy, reproducibility, and decision support for clinical and research teams.
July 2025 monthly summary for childhealthbiostatscore/CHCO-Code focusing on business value, technical achievements, and demonstrated skills. Highlights include feature delivery, bug fixes, code quality improvements, and expanded analysis capabilities that collectively enhance data reliability, insights, and presentation of results.
July 2025 monthly summary for childhealthbiostatscore/CHCO-Code focusing on business value, technical achievements, and demonstrated skills. Highlights include feature delivery, bug fixes, code quality improvements, and expanded analysis capabilities that collectively enhance data reliability, insights, and presentation of results.
June 2025 monthly summary for CHCO-Code. Focused on end-to-end analytics improvements across ATTEMPT and CROCODILE analyses, with emphasis on visualization, reporting, data processing, and data quality. Delivered multiple ATTEMPT enhancements, introduced CROCODILE nebula visuals with GSEA plots, and produced the IMPROVE study missingness/outlier report. Fixed critical data loading and filtering issues to ensure reliable visualizations and analyses. Created a QUARTO-based ATTEMPT scRNA presentation/report to support stakeholder communication and publication-ready outputs. These efforts increased analytical capability, data quality, and business value by enabling richer clinical insights and more reliable reporting.
June 2025 monthly summary for CHCO-Code. Focused on end-to-end analytics improvements across ATTEMPT and CROCODILE analyses, with emphasis on visualization, reporting, data processing, and data quality. Delivered multiple ATTEMPT enhancements, introduced CROCODILE nebula visuals with GSEA plots, and produced the IMPROVE study missingness/outlier report. Fixed critical data loading and filtering issues to ensure reliable visualizations and analyses. Created a QUARTO-based ATTEMPT scRNA presentation/report to support stakeholder communication and publication-ready outputs. These efforts increased analytical capability, data quality, and business value by enabling richer clinical insights and more reliable reporting.
May 2025 (CHCO-Code) focused on cross-project data harmonization, robust reporting, and enhanced visualization to enable faster sharing and clearer business insights. Key outcomes include: 1) Gomez data harmonization scripts implemented in R and Python to compute renal physiology parameters (e.g., filtration fraction, glomerular pressure, arteriolar resistances) for standardized data sharing across studies; 2) refactored PANTHER/PENGUIN data cleaning and analysis pipelines with updated variable mappings, data filtering, added analysis variables, and improved visualization readiness; 3) comprehensive ATTEMPT visualization improvements enabling volcano plots and GSEA results across multiple models and cell types, with support for offset methods (REML, TMM, pooled); 4) ATTEMPT scRNA correlation analytics—integrating scRNA data with clinical datasets and producing cross-domain visualizations (pathway analyses, z-score plots) across cell types; 5) RH2 results reporting enhancements—adding RH2 results for Phoom/Diego and refining reporting by excluding Lean Control data for more granular RH/RH2 insights. Additional notable work included PB90 cohort data subset creation (Long PB90) with updated data loading paths and a new CSV output, and a bug fix in EDNSG 2025 data filtering to use the x column correctly. Overall impact: higher data quality, reproducibility, cross-project visibility, and actionable reporting; demonstrated growth in data wrangling, visualization, and cross-domain analytics.
May 2025 (CHCO-Code) focused on cross-project data harmonization, robust reporting, and enhanced visualization to enable faster sharing and clearer business insights. Key outcomes include: 1) Gomez data harmonization scripts implemented in R and Python to compute renal physiology parameters (e.g., filtration fraction, glomerular pressure, arteriolar resistances) for standardized data sharing across studies; 2) refactored PANTHER/PENGUIN data cleaning and analysis pipelines with updated variable mappings, data filtering, added analysis variables, and improved visualization readiness; 3) comprehensive ATTEMPT visualization improvements enabling volcano plots and GSEA results across multiple models and cell types, with support for offset methods (REML, TMM, pooled); 4) ATTEMPT scRNA correlation analytics—integrating scRNA data with clinical datasets and producing cross-domain visualizations (pathway analyses, z-score plots) across cell types; 5) RH2 results reporting enhancements—adding RH2 results for Phoom/Diego and refining reporting by excluding Lean Control data for more granular RH/RH2 insights. Additional notable work included PB90 cohort data subset creation (Long PB90) with updated data loading paths and a new CSV output, and a bug fix in EDNSG 2025 data filtering to use the x column correctly. Overall impact: higher data quality, reproducibility, cross-project visibility, and actionable reporting; demonstrated growth in data wrangling, visualization, and cross-domain analytics.
Monthly work summary for 2025-04 focusing on delivering end-to-end data analysis and data integration pipelines for CHCO-Code, with a strong emphasis on data harmonization, pathway analysis readiness, and reproducible tooling across MRI and REDCap studies.
Monthly work summary for 2025-04 focusing on delivering end-to-end data analysis and data integration pipelines for CHCO-Code, with a strong emphasis on data harmonization, pathway analysis readiness, and reproducible tooling across MRI and REDCap studies.
March 2025 performance summary for childhealthbiostatscore/CHCO-Code. Delivered four core features across data integration, reporting, and analytics, strengthening data quality and enabling broader biological insights for high-priority studies. Key outcomes include: refined PANTHER baseline analysis and RPPR 2025 reporting with improved data processing, BMI percentile risk grouping, and race/ethnicity reporting; integrated ATTEMPT SomaScan and Olink data into a harmonized dataset with enhanced emmeans visualization across cell types and expanded GSEA/volcano plotting for ATTEMPT analyses and protein reporting; prepared RH2/CRC data for CC DECODE with harmonized datasets and subset-ready exports; and implemented Dapagliflozin vs Placebo treatment-effect analyses using emmeans and GSEA across time points and cell types. These efforts improved data quality, expanded analytical coverage, and accelerated actionable insights for clinical and translational goals.
March 2025 performance summary for childhealthbiostatscore/CHCO-Code. Delivered four core features across data integration, reporting, and analytics, strengthening data quality and enabling broader biological insights for high-priority studies. Key outcomes include: refined PANTHER baseline analysis and RPPR 2025 reporting with improved data processing, BMI percentile risk grouping, and race/ethnicity reporting; integrated ATTEMPT SomaScan and Olink data into a harmonized dataset with enhanced emmeans visualization across cell types and expanded GSEA/volcano plotting for ATTEMPT analyses and protein reporting; prepared RH2/CRC data for CC DECODE with harmonized datasets and subset-ready exports; and implemented Dapagliflozin vs Placebo treatment-effect analyses using emmeans and GSEA across time points and cell types. These efforts improved data quality, expanded analytical coverage, and accelerated actionable insights for clinical and translational goals.
February 2025: Delivered end-to-end analytics and data engineering enhancements in the CHCO-Code repository, focusing on cross-study consistency, scalable single-cell analyses, and enhanced reporting. Implemented liver analysis refinements and established a liver single-cell RNA sequencing workflow, enabling more accurate liver-focused insights. Executed multi-study data harmonization and integration across biopsy, pathology, and DECODE data pipelines, improving cross-study comparability and data quality. Advanced ATTEMPT scRNA analyses, including descriptive tables, pathway analyses, comprehensive data ingestion, and harmonization scripts to support broader RNA-seq studies. Strengthened RPC2 data extraction with descriptive tables and REDCap data pull/harmonization, increasing data availability for RPC2 study reporting. Enhanced PANTHER analysis with risk-group and sex/Tanner-stage stratifications and updated RPPR 2025 reporting processes, improving interpretability and regulatory readiness across studies.
February 2025: Delivered end-to-end analytics and data engineering enhancements in the CHCO-Code repository, focusing on cross-study consistency, scalable single-cell analyses, and enhanced reporting. Implemented liver analysis refinements and established a liver single-cell RNA sequencing workflow, enabling more accurate liver-focused insights. Executed multi-study data harmonization and integration across biopsy, pathology, and DECODE data pipelines, improving cross-study comparability and data quality. Advanced ATTEMPT scRNA analyses, including descriptive tables, pathway analyses, comprehensive data ingestion, and harmonization scripts to support broader RNA-seq studies. Strengthened RPC2 data extraction with descriptive tables and REDCap data pull/harmonization, increasing data availability for RPC2 study reporting. Enhanced PANTHER analysis with risk-group and sex/Tanner-stage stratifications and updated RPPR 2025 reporting processes, improving interpretability and regulatory readiness across studies.
Month: 2025-01. This period delivered end-to-end data and analytics improvements for the CHCO-Code repository, focusing on enabling deeper treatment-arm analyses, standardizing data pipelines, and strengthening visualization reliability. Key features delivered include ATTEMPT Dataset and Visualization Enhancements with local MRI data integration and IPA/TAL pathway visuals, Data Harmonization and Brain Biomarker Labeling Improvements, HbA1c Lab Data Integration from RPC2, ATTEMPT scRNA Analysis Modernization using SingleCellExperiment with QC/normalization/scaling/MAST/de pathway enrichment, and Panther Analysis Plotting Fix with a serialization guard. These efforts improve data quality, cross-study comparability, and reliability of analytical outputs, enabling better decision-making and more efficient research workflows.
Month: 2025-01. This period delivered end-to-end data and analytics improvements for the CHCO-Code repository, focusing on enabling deeper treatment-arm analyses, standardizing data pipelines, and strengthening visualization reliability. Key features delivered include ATTEMPT Dataset and Visualization Enhancements with local MRI data integration and IPA/TAL pathway visuals, Data Harmonization and Brain Biomarker Labeling Improvements, HbA1c Lab Data Integration from RPC2, ATTEMPT scRNA Analysis Modernization using SingleCellExperiment with QC/normalization/scaling/MAST/de pathway enrichment, and Panther Analysis Plotting Fix with a serialization guard. These efforts improve data quality, cross-study comparability, and reliability of analytical outputs, enabling better decision-making and more efficient research workflows.
2024-12 monthly summary for CHCO-Code: Delivered end-to-end data integration, imaging harmonization, and multi-omics analysis enhancements across ATTEMPT, PB90, and PANTHER studies. Key work includes a data loading refactor, expanded reporting, and the introduction of T1 mapping within harmonized data. Specific features completed: ATTEMPT study – data integration with T1 mapping, imaging harmonization, a spatial transcriptomics subset, differential expression analysis, enhanced reporting, descriptive tables, and separation of ATTEMPT analysis reports; PB90 study – script to merge scRNA sequencing metadata with clinical data, cleaning, and exporting the merged metadata; PANTHER study – T1 mapping integration with derived variables, log transformations, and updated MRI-related parameters. Also implemented repeated measures handling improvements and improved ID matching across spatial-imaging datasets to reduce downstream errors. Overall impact includes higher data quality, reproducibility, faster downstream analyses, and clearer cross-study reporting, delivering tangible business value for research milestones and decision support.
2024-12 monthly summary for CHCO-Code: Delivered end-to-end data integration, imaging harmonization, and multi-omics analysis enhancements across ATTEMPT, PB90, and PANTHER studies. Key work includes a data loading refactor, expanded reporting, and the introduction of T1 mapping within harmonized data. Specific features completed: ATTEMPT study – data integration with T1 mapping, imaging harmonization, a spatial transcriptomics subset, differential expression analysis, enhanced reporting, descriptive tables, and separation of ATTEMPT analysis reports; PB90 study – script to merge scRNA sequencing metadata with clinical data, cleaning, and exporting the merged metadata; PANTHER study – T1 mapping integration with derived variables, log transformations, and updated MRI-related parameters. Also implemented repeated measures handling improvements and improved ID matching across spatial-imaging datasets to reduce downstream errors. Overall impact includes higher data quality, reproducibility, faster downstream analyses, and clearer cross-study reporting, delivering tangible business value for research milestones and decision support.
Month 2024-11 - CHCO-Code: Delivered substantial features enhancements and data quality improvements across ROCKIES/RH analyses and data harmonization, with targeted RPC2 cohort prep. Achieved reliable PET+scRNA workflows, expanded data variables, and improved downstream data processing; produced a reusable, Lambda-enabled pipeline and robust baseline cohorts. Overall business value realized via richer analytics, faster insights, and improved data integrity.
Month 2024-11 - CHCO-Code: Delivered substantial features enhancements and data quality improvements across ROCKIES/RH analyses and data harmonization, with targeted RPC2 cohort prep. Achieved reliable PET+scRNA workflows, expanded data variables, and improved downstream data processing; produced a reusable, Lambda-enabled pipeline and robust baseline cohorts. Overall business value realized via richer analytics, faster insights, and improved data integrity.
October 2024 monthly summary for CHCO-Code: Delivered end-to-end Differential Gene Expression (DGE) analysis setup for ROCKIES and Liver scRNA-seq via new R Markdown workflows. Implemented scalable, reproducible pipelines with parallel processing across Seurat objects, enabling cross-cell-type comparisons and clinical stratification.
October 2024 monthly summary for CHCO-Code: Delivered end-to-end Differential Gene Expression (DGE) analysis setup for ROCKIES and Liver scRNA-seq via new R Markdown workflows. Implemented scalable, reproducible pipelines with parallel processing across Seurat objects, enabling cross-cell-type comparisons and clinical stratification.
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