BMI 6313: Scientific Writing in Healthcare

This repository contains details about the bioinformatics project performed using iDEP as part of the BMI 6313: Scientific Writing in Healthcare course. The study, titled "Long-term IQOS exposure modulates apoptosis and cytokine signaling pathways: Genomic insights from mouse lungs", analyzed the genomic effects of IQOS exposure on mouse lungs using public microarray dataset obtained from NCBI GEO.

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Long-term IQOS Exposure Modulates Apoptosis and Cytokine Signaling Pathways: Genomic Insights from Mouse Lungs

Project Overview

1. Data Retrieval

The mouse microarray dataset with the the accession ID GSE161869 was accessed through the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO).

• Data retrieval was conducted using the GEOquery R package.
• Sample groups: Control, IQOS-exposed, and Cigarette Smoke (CS)-exposed samples (n=3 per group).
• Subsequent analyses were performed on iDEP versions 0.96 and 2.01.

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2. Exploratory Data Analysis (EDA)

• Tools used: iDEP version 0.96
• Methods:
  • Hierarchical clustering (heatmap) with average linkage and correlation-based distance measures.
  • Principal Component Analysis (PCA) for dimensionality reduction.
  • Quantile normalization of gene expression levels during data preprocessing.

Figures:

• Heatmap
• PCA Plot
• Quantile Normalization plot

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3. Differential Expression and Functional Enrichment Analysis

• Tools used: iDEP version 0.96, limma R package.
  1. IQOS-exposed vs. Control
  2. IQOS-exposed vs. CS-exposed
• Criteria for Differentially Expressed Genes (DEGs):
  • False Discovery Rate (FDR) ≤ 0.05
  • |Fold Change| > 1.5
• Functional enrichment analyses:
  • GO Biological Process (BP) and KEGG pathway enrichments using Gene Set Enrichment Analysis (GSEA).

Figures:

• DEG Bar Plot

• IQOS-exposed vs. Control Volcano Plot

• IQOS-exposed vs. CS-exposed Volcano Plot

• DEG Venn Diagram

• Gene Ontology Biological Process Enrichment Tree plot

• KEGG Enrichment Tables

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4. Network Analysis

• Tools used:
  • iDEP version 2.01 for Weighted Gene Co-Expression Network Analysis (WGCNA).
  • StringDB version 12 for network visualization.
• Key Parameters:
  • Soft threshold = 5
  • Minimum module size = 20
• Functional enrichment for gene co-expression modules was conducted using GO BP and KEGG analyses.

Figures:

• WGCNA Coexpressed Gene Modules and Enrichment
• Apoptosis StringDB PPI
• Cytokine Production StringDB PPI

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Citation

If you use the tools or dataset mentioned in this repository in your research, please cite the following references:

• Barrett, T., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., Marshall, K. A., Phillippy, K. H., Sherman, P. M., Holko, M., Yefanov, A., Lee, H., Zhang, N., Robertson, C. L., Serova, N., Davis, S., & Soboleva, A. (2013). NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Research, 41(D1), D991–D995. https://doi.org/10.1093/NAR/GKS1193

• Ge, S. X., Son, E. W., & Yao, R. (2018). iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinformatics 2018 19:1, 19(1), 1–24. https://doi.org/10.1186/S12859-018-2486-6

• Nitta, N. A., Sato, T., Komura, M., Yoshikawa, H., Suzuki, Y., Mitsui, A., Kuwasaki, E., Takahashi, F., Kodama, Y., Seyama, K., & Takahashi, K. (2022). Exposure to the heated tobacco product IQOS generates apoptosis-mediated pulmonary emphysema in murine lungs. American Journal of Physiology - Lung Cellular and Molecular Physiology, 322(5), L699–L711. https://doi.org/10.1152/ajplung.00215.2021

• Sean, D., & Meltzer, P. S. (2007). GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics, 23(14), 1846–1847. https://doi.org/10.1093/BIOINFORMATICS/BTM254

• Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., Gable, A. L., Fang, T., Doncheva, N. T., Pyysalo, S., Bork, P., Jensen, L. J., & Von Mering, C. (2023). The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research, 51(D1), D638–D646. https://doi.org/10.1093/NAR/GKAC1000

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For questions or issues, please contact the repository maintainer. Refer to the relavant Poster along with the final course paper (via QR code) for detailed information and results.

This repository is solely for educational purposes and serves as a backup for my graduate school assignments related to the BMI 6313: Scientific Writing in Healthcare course at McWilliams School of Biomedical Informatics at UTHealth Houston.