
Over seven months, contributed to the stjude/proteinpaint repository by building and refining bioinformatics workflows for gene expression and enrichment analysis. Developed robust differential expression and gene set analysis features, integrating R and JavaScript for both backend computation and interactive UI. Enhanced data validation, serialization, and error handling to ensure analysis accuracy and reproducibility, while expanding automated testing in Rust and TypeScript for regression safety. Introduced new visualization tools, improved data persistence, and streamlined pipeline reliability through code refactoring and expanded test coverage. This work enabled faster, more reliable RNA-seq analytics and improved user experience for researchers and clinicians.
Consolidated testing improvements for the proteinpaint project in May 2025, focusing on the differential expression (Wilcoxon) workflow and HDF5 data access. Implemented and stabilized unit tests in the Rust modules (Wilcoxon DE analysis and DEanalysis) and resolved test_examples.rs issues to ensure reliable test execution. These efforts improve regression safety, reduce debugging time, and uplift release confidence for the proteinpaint pipeline.
Consolidated testing improvements for the proteinpaint project in May 2025, focusing on the differential expression (Wilcoxon) workflow and HDF5 data access. Implemented and stabilized unit tests in the Rust modules (Wilcoxon DE analysis and DEanalysis) and resolved test_examples.rs issues to ensure reliable test execution. These efforts improve regression safety, reduce debugging time, and uplift release confidence for the proteinpaint pipeline.
April 2025 performance summary for stjude/proteinpaint. Delivered robust data-serialization improvements, stronger validation in differential expression workflows, expanded test infrastructure for RNA-seq analyses, and a safe-fail guard against empty gene-count matrices. These changes enhance data fidelity, reduce downstream errors, and improve test coverage and release confidence.
April 2025 performance summary for stjude/proteinpaint. Delivered robust data-serialization improvements, stronger validation in differential expression workflows, expanded test infrastructure for RNA-seq analyses, and a safe-fail guard against empty gene-count matrices. These changes enhance data fidelity, reduce downstream errors, and improve test coverage and release confidence.
March 2025 monthly summary for stjude/proteinpaint: Delivered major enhancements across visualization, analytics workflows, and data I/O. Implemented MDS plotting with improved margins and optional generation for small datasets; added server-side image generation for edgeR outputs with CPM cutoffs and benchmarking; introduced tentative read-file input support and image import reliability improvements; integrated limma-voom into the workflow and documented Wilcoxon test with default metadata and TMM scaling; added a plotting script to automate result visualization. Performed codebase cleanup including removal of VarGenes, addressed R code bugs, filtered p-values of zero, and ensured p-values are passed in linear scale for R and Rust. These changes improve accuracy, scalability, and user productivity, enabling faster, more reliable downstream analyses and reporting.
March 2025 monthly summary for stjude/proteinpaint: Delivered major enhancements across visualization, analytics workflows, and data I/O. Implemented MDS plotting with improved margins and optional generation for small datasets; added server-side image generation for edgeR outputs with CPM cutoffs and benchmarking; introduced tentative read-file input support and image import reliability improvements; integrated limma-voom into the workflow and documented Wilcoxon test with default metadata and TMM scaling; added a plotting script to automate result visualization. Performed codebase cleanup including removal of VarGenes, addressed R code bugs, filtered p-values of zero, and ensured p-values are passed in linear scale for R and Rust. These changes improve accuracy, scalability, and user productivity, enabling faster, more reliable downstream analyses and reporting.
February 2025 — Delivered key DE visualization and gene-set analysis enhancements in stjude/proteinpaint, with robust UI improvements, BlitzGSEA integration behind serverconfig flags, data persistence capabilities, and targeted refactors to boost reliability and maintainability. These changes provide faster, more accurate interpretation of differential expression results, safer feature toggles, and a cleaner codebase for future work.
February 2025 — Delivered key DE visualization and gene-set analysis enhancements in stjude/proteinpaint, with robust UI improvements, BlitzGSEA integration behind serverconfig flags, data persistence capabilities, and targeted refactors to boost reliability and maintainability. These changes provide faster, more accurate interpretation of differential expression results, safer feature toggles, and a cleaner codebase for future work.
January 2025 monthly summary for stjude/proteinpaint: Delivered robust EdgeR-based differential expression analysis enhancements, added correlation analysis capability via a new corr.R script, and improved diagnostics visibility. Also addressed code quality and stability with targeted fixes, improving reliability of analyses and debuggability for data scientists and clinicians.
January 2025 monthly summary for stjude/proteinpaint: Delivered robust EdgeR-based differential expression analysis enhancements, added correlation analysis capability via a new corr.R script, and improved diagnostics visibility. Also addressed code quality and stability with targeted fixes, improving reliability of analyses and debuggability for data scientists and clinicians.
December 2024 monthly summary for stjude/proteinpaint highlighting DE analysis enhancements with UI and server integration, plus confounding-factor support. Delivered core DE features with UI options for edgeR vs Wilcoxon, server-side parameter handling, and refined p-value processing; and added confounding-factor support with a design matrix, confounder data handling, and client/server integration. These changes improve analysis accuracy, flexibility, and user workflow while maintaining performance and traceability through structured commits.
December 2024 monthly summary for stjude/proteinpaint highlighting DE analysis enhancements with UI and server integration, plus confounding-factor support. Delivered core DE features with UI options for edgeR vs Wilcoxon, server-side parameter handling, and refined p-value processing; and added confounding-factor support with a design matrix, confounder data handling, and client/server integration. These changes improve analysis accuracy, flexibility, and user workflow while maintaining performance and traceability through structured commits.
Delivered targeted enhancements to GSEA and geneORA workflows in stjude/proteinpaint, reinforcing data quality and user experience. Key features include GSEA results enhancements with header/context integration, updated p-value threshold, and gene set size filtering; gene set size column reordering in geneORA; and a UI-backed non-coding gene filter for GSEA. Major fixes included disabling the Differential Expression button until two groups are specified and preventing duplicate plot windows for GSEA/geneORA plots. The work improves analysis accuracy, readability, and reliability, enabling faster, more trustworthy enrichment analysis across projects. Technologies demonstrated include front-end JavaScript/UI (DEanalysis, charts.js), back-end data filtering, and robust UI state management.
Delivered targeted enhancements to GSEA and geneORA workflows in stjude/proteinpaint, reinforcing data quality and user experience. Key features include GSEA results enhancements with header/context integration, updated p-value threshold, and gene set size filtering; gene set size column reordering in geneORA; and a UI-backed non-coding gene filter for GSEA. Major fixes included disabling the Differential Expression button until two groups are specified and preventing duplicate plot windows for GSEA/geneORA plots. The work improves analysis accuracy, readability, and reliability, enabling faster, more trustworthy enrichment analysis across projects. Technologies demonstrated include front-end JavaScript/UI (DEanalysis, charts.js), back-end data filtering, and robust UI state management.

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