Multi-omics-MachineLearning-in-IPF

This repository contains R scripts for bulk transcriptomics analysis, WGCNA, single-cell transcriptomics, and machine-learning modeling in idiopathic pulmonary fibrosis (IPF).

Code structure

• Analysis.GSE32537+GSE110147.R
  Bulk GEO data analysis script for IPF/control cohorts (data loading, basic preprocessing, and differential expression outputs).

• Analysis.WGCNA.R
  WGCNA pipeline for identifying co-expression modules and relating modules to clinical traits.

• Analysis.Single-cell.Transcriptomic.R
  Single-cell RNA-seq analysis workflow (Seurat-based processing, integration/clustering, and cell-type annotation).

• pipline.Machine.learning.R
  Core machine-learning function library (model training utilities, feature handling, prediction, and evaluation helpers).
  This file is intended to be sourced by other ML scripts.

• Output.Machine.learning.R
  End-to-end machine-learning run script that uses the pipeline functions to train models and generate prediction/performance outputs.

Notes

• Scripts are designed to be run independently, depending on which analysis you need (bulk, WGCNA, scRNA-seq, or ML).
• Update file paths at the top of each script to match your local data location.