🧬 Making Sense of RNA-seq Data: Differential Gene Expression and Pathway Analysis (UBDS3_2025 Edition)

Instructor: Ian Kouzel TAs: Anastasiia Horlova and Oleksii Stroganov

UBDS³ Ukrainian Biological Data Science Summer School Location: Uzhhorod, Ukraine

Dates: 27.07-1.08.2025

Course Schedule

Schedule

Purpose

This course guides you through differential gene expression analysis—from raw gene count data obtained via RNA-seq experiments to extracting meaningful biological insights. You'll work with real-world datasets to perform exploratory analysis, differential gene expression analysis, and enrichment analysis (including GO, KEGG, Reactome, DOSE, and WikiPathways).

You'll learn how to import and explore data in R/RStudio using the tidyverse, analyze RNA-seq count data with DESeq2, and get hands-on experience with the Bioconductor packages. By the end, you’ll turn your analysis into a fully reproducible R Markdown report.

Projects

💡 Projects will be disctributed by instructor and TAs among the participants

1. Epithelial mesenchymal transition (EMT)

Study: Epithelial mesenchymal transition (EMT) in A549 NSCLC cells. TGFbeta was used to induce EMT, RNA isolated and subjected to RNAseq on Illumina HiSeq.

From the authors: The capacity of cancer cells to undergo epithelial mesenchymal trans-differentiation has been implicated as a factor driving metastasis, through the acquisition of enhanced migratory/invasive cell programs and the engagement of anti-apoptotic mechanisms promoting drug and radiation resistance. Our aim was to define molecular signaling changes associated with mesenchymal trans-differentiation in two KRas mutant NSCLC models. We focused on central transcription and epigenetic regulators predicted to be important for mesenchymal cell survival.

📄 References:

1. Haley, J.A. et al. (2014). Altered Transcriptional Control Networks with Trans-Differentiation of Isogenic Mutant KRas NSCLC Models. Frontiers in Oncology BioProject: PRJNA515936
2. Hao, Y., Baker, D., & ten Dijke, P. (2019). TGF-β-mediated epithelial–mesenchymal transition and cancer metastasis. International Journal of Molecular Sciences, 20(11), 2767. https://doi.org/10.3390/ijms20112767 [REVIEW, additional reading]

🧪 Experimental Design for Project 1 (EMT)

Sample ID	Condition
SRR8460397	TGFbeta3
SRR8460398	TGFbeta3
SRR8460399	Control
SRR8460400	Control

💡 General instructions for downloading: not required to do it right away. Will be discussed in detail in Lab 2.

# Download salmon quantification files for EMT dataset
wget "https://www.dropbox.com/scl/fo/i387hjzocw227bjllf069/AB0Y-Rt3DxCAXMCJKJVGppM?rlkey=4feunl032pfpbr69w9yfj4al4&st=l2deogkv&dl=1" -O TGFbeta_data.zip
# Decompress downloaded folder
unzip TGFbeta_data.zip -d TGFbeta_data

2. Glioblastoma [GSE147352, PRJNA798408]

Study: Phenotypic and molecular states of IDH1 mutation-induced CD24-positive glioma stem-like cells.

From the authors: Mutations in IDH1 and IDH2 drive the development of gliomas. These genetic alterations promote tumor cell renewal, disrupt differentiation states, and induce stem-like properties. Understanding how this phenotypic reprogramming occurs remains an area of high interest in glioma research. Our data demonstrate that induction of a CD24-positive population is one mechanism by which IDH-mutant tumors acquire stem-like properties. These findings have significant implications for our understanding of the molecular underpinnings of IDH-mutant gliomas. vs. normal

📄 References:

1. Haddock, S. et al. (2022). Phenotypic and molecular states of IDH1 mutation-induced CD24-positive glioma stem-like cells. Neoplasia, 28

🧪 Experimental Design for Project 2 (Glioblastoma)

Sample ID	Condition
glioblastoma_SRR11365267	glioblastoma
glioblastoma_SRR11365268	glioblastoma
glioblastoma_SRR11365269	glioblastoma
normal_SRR11365252	normal
normal_SRR11365253	normal
normal_SRR11365254	normal
...	...

💡 General instructions for downloading: not required to do it right away. Will be discussed in detail in Lab 2.

wget "https://www.dropbox.com/scl/fi/6xx7jp9mr320434ybt10p/salmon_quantification.zip?rlkey=crnuuot35wjnfvxb9vyo6cgs2&st=54lr8iv8&dl=0" -O Glioblastoma_GSE147352.zip
# Decompress downloaded folder
unzip Glioblastoma_GSE147352.zip -d Glioblastoma_GSE147352

3. Schizophrenia [GSE63738]

💡 General instructions for downloading: not required to do it right away. Will be discussed in detail in Lab 2.

# Download salmon quantification files for schizophrenia_GSE63738 dataset
wget "https://www.dropbox.com/scl/fo/u9bbd2p4zub4zh1zz52q4/AHS29ectp5VKqNEgNNGAWMQ?rlkey=r4fey782143i1x7lt0t0jpuav&st=uj1har82&dl=0" -O schizophrenia_GSE63738.zip
# Decompress downloaded folder
unzip schizophrenia_GSE63738.zip -d schizophrenia_GSE63738

🧪 Experimental Design for Project 3 (Schizophrenia)

Sample ID	Condition
SRR1686362	Control
SRR1686363	Control
SRR1686364	Control
SRR1686365	Control
SRR1686366	Control
SRR1686367	Control
SRR1686368	Schizophrenia
SRR1686369	Schizophrenia
SRR1686370	Schizophrenia
SRR1686371	Schizophrenia
SRR1686372	Schizophrenia
SRR1686373	Schizophrenia

💻 Labs

All lab instructions and code are located in the Labs folder.

👉 Go to Labs Folder to start with labs

⚙️ Generate htmls or pdfs from md files (in case required)

# Option1: save manually as a page
# Option2: render with pandoc
# tested on MacOS
brew install pandoc
# html (better)
pandoc README.md -o README.html
# pdf (not everything will be rendered)
pandoc README.md -o README.pdf --pdf-engine=xelatex

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✅ Required R packages

# CRAN
tidyverse
DT
# Bioconductor
tximport
DESeq2
AnnotationDbi
org.Hs.eg.db
clusterProfiler
pathview
enrichplot
DOSE
ReactomePA
shiny

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📚 Resources

• DESeq2 Vignette
• clusterProfiler
• GO analysis
• KEGG analysis
• Wikipathways analysis
• REACTOME
• DOSE
• R for Data Science
• Mastering Shiny