TheDongLab/AI2AMP-PD: AI2AMP-PD v1.0

Abstract

This is the first public release of the analysis code accompanying the manuscript "Differential expression and machine-learning classification of Parkinson's disease using AMP-PD RNA-seq data" (Hu R, Dong X, et al.). Overview Analysis pipeline for RNA-seq–based differential expression (DE) and machine-learning (ML) classification of Parkinson's disease (PD) case/control status, using AMP-PD cohorts PPMI and PDBP/BioFIND (PDBF). What's included Data preparation — build case/control and mutation-carrier sample sets and extract per-cohort RNA-seq quantification matrices (DE/src/filtration.py, DE/src/extract_quant.py) Differential expression — DESeq2-based DE analysis for genes, eRNAs, and circRNAs, adjusted for covariates (age, sex, plate, RIN, genotype PCs) (DE/src/DE_PCs.R, DE/src/eRNA/, DE/src/circRNA/) Functional enrichment — GO/KEGG/Reactome/GSEA analysis on DE results (DE/src/Enrich_Profiler.R) ML classification — logistic regression, LASSO, SVM, random forest, XGBoost, KNN, MLP, and stepwise feature-addition classifiers trained on PPMI and independently tested on PDBF (ML/src/*.py) Model evaluation — combined AUROC/PR curve and feature-importance summary plots (ML/src/31_plots_AUC_PR_inOne.py) NanoString QC — standalone endogenous/housekeeping gene QC script (DE/NanoStringData/) Recorded R/Python session info and pinned dependencies (session_info/) Requirements R ≥ 4.0 (tidyverse, DESeq2, clusterProfiler, and related Bioconductor/CRAN packages) Python ≥ 3.8 (pandas, numpy, scikit-learn, matplotlib, seaborn) Known limitations Scripts are tailored to the specific AMP-PD sample sets and covariate schemas used in this study; not general-purpose tools File paths and cohort-specific parameters are set via literals/CLI args and may need editing for new datasets Raw AMP-PD data are not distributed with this repository; access requires a separate application to the AMP-PD Knowledge Portal (https://amp-pd.org/) License MIT License