The document provides an overview of gene expression analysis with R, emphasizing the importance of RNA sequencing (RNA-seq) data and the tools used, such as DESeq2 and edgeR. It covers data preparation, normalization methods, differential gene expression analysis, and visualization techniques, including examples of R code for each step. Various visual aids are included to illustrate the processes and results, such as diagrams, plots, and code snippets.

1. Gene Expression
Analysis with R

2. • Slide 1: Introduction to Gene Expression Analysis
• Title: Introduction to Gene Expression Analysis with R
• Content:
• Overview of gene expression analysis.
• Importance of analyzing RNA sequencing (RNA-seq) data.
• Mention tools and packages used: DESeq2, edgeR, limma, and
Bioconductor.
• Focus on workflows: normalization, differential expression, and
visualization.
• Visual: Diagram of RNA-seq data processing pipeline.

3. • Slide 2: Preparing the Data
• Title: Data Preparation in R
• Content:
• Loading count data and sample metadata.
• Using DESeq2 or edgeR for preprocessing.
• Example code to load data:r
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• counts <- read.csv("gene_counts.csv", row.names=1) meta <-
read.csv("metadata.csv", row.names=1)
• Visual: Screenshot or table showing gene count data and sample
metadata structure.

4. • Slide 3: Normalizing the Data
• Title: Normalization Methods
• Content:
• Importance of normalization in gene expression data.
• Explain methods: rlog, vst (variance stabilization transformation), or TMM.
• Example code for normalization:r
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• dds <- DESeqDataSetFromMatrix(countData = counts, colData = meta,
design = ~ condition) dds <- DESeq(dds) norm_counts <- counts(dds,
normalized = TRUE)
• Visual: Plot showing raw vs normalized data.

5. • Slide 4: Differential Gene Expression Analysis
• Title: Performing Differential Expression Analysis
• Content:
• Steps to identify differentially expressed genes.
• Running DESeq2 for differential analysis.
• Example code:r
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• res <- results(dds) res <- res[order(res$pvalue), ]
• Define thresholds for significance (p-value, fold change).
• Visual: Volcano plot or MA plot showing differentially expressed
genes.

6. • Slide 5: Visualization of Results
• Title: Visualizing Gene Expression Results
• Content:
• Plotting heatmaps and PCA for visualizing data patterns.
• Example code for heatmap:r
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• library(pheatmap) pheatmap(assay(vst(dds))[top_genes, ])
• Example code for PCA:r
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• plotPCA(vst(dds), intgroup = "condition")
• Visual: Heatmap and PCA plot showing gene clustering and sample
separation.