Meta-Analysis for jamovi
pyLocusZoom -- publication-ready GWAS visualization in Python: LocusZoom-style regional association plots with LD coloring, gene tracks and recombination overlays, plus Manhattan, QQ, Miami, eQTL, fine-mapping, PheWAS and forest plots. Dog and cat genomes built in.
Applied a Cox Proportional Hazards model in Python to identify prognostic factors associated with survival in pediatric candidemia patients. Estimated hazard ratios, evaluated clinical covariates, and interpreted survival outcomes.
Publication-ready research figures from a single command: forest plots, KM curves with risk tables, PRISMA 2020, ROC with DeLong — 21 chart types, 9 journal presets, Chinese support, MIT-0
R notebook for the association of seven clinical biomarkers with BMI and diagnosed diabetes in NHANES 2017-2018 (R). Follows the structure of Nightingale Health's open ggforestplot tutorial.
Reproducible R and Python clinical trial figure templates, synthetic teaching data, independent numerical QC, and Clinical Data Lab demos.
Forest plot of Cox hazard ratios, the standard way to report which factors change patient risk.
Survival analysis, Cox regression and meta-analysis on a classic lung cancer dataset, with a statistical analysis plan and paper-style report.
Modular publication-figure toolkit for single-cell, spatial and clinical omics — Python (matplotlib) + R (ggplot2). Swappable themes; every panel runs on synthetic data.
Provides biomedical plotting archetypes fully interoperable with the matplotlib API.
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