This repository contains code and analysis for multi-omic data related to Type 2 Diabetes (T2D). The project integrates genomic, transcriptomic, and proteomic data to provide a comprehensive understanding of the molecular mechanisms underlying T2D.
- Data Processing: Scripts for processing and normalizing multi-omic data.
- Differential Gene Expression (DGE): Analysis of gene expression differences between diabetic and non-diabetic samples.
- Gene Set Enrichment Analysis (GSEA): Identification of significantly enriched pathways.
- Proteomics Analysis: Quantitative analysis of protein expression data.
- Visualization: Generation of heatmaps, PCA plots, and other visualizations to represent the data and results.
Alignment.sh: Script for aligning sequence data with HISAT2
Counting.sh: Script for counting features with feature counts
DGE-Figures.R: R script for generating DGE figures
DGE_Analysis.Rmd: RMarkdown for differential gene expression analysis
GSEA_Analysis.Rmd: RMarkdown for gene set enrichment analysis
GSEA_Figures.R: R script for generating GSEA figures
Importing_salmon_output.R: R script for importing salmon quantification results and saving
Part2_Phyloseq_moretrim.Rmd: RMarkdown for phylogenetic analysis
SalmonQuant.sh: Script for quantifying transcripts with Salmon
T2D_Proteomics.Rmd: RMarkdown for proteomics analysis
T2D_metprotgen_xMWAS.Rmd: RMarkdown for integrated metabolomics, proteomics, and genomics analysis
fastqc.sh: Script for quality control of sequence data
phyloseqobject_moretrim.RDS: RDS file for phyloseq object
trimfirst7.sh: Script for trimming sequences
triming.sh: Another script for trimming sequences
The analysis uses multi-omic data including genomic, transcriptomic, and proteomic data.
The project generates various visualizations, including heatmaps, volcano plots, PCA plots, and enrichment plots,for understanding the molecular mechanisms underlying Type 2 Diabetes.
Special thanks to Emory University for the opportunity to work on this project.