slides on Gene Expression Analysis with R:

gene expression analysis workshop.

1. Gene Expression Analysis with R

2. 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. 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 Copy code 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. 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 Copy code 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. Differential Gene Expression Analysis Title : Performing Differential Expression Analysis Content : Steps to identify differentially expressed genes. Running DESeq2 for differential analysis. Example code:r Copy code 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. Visualization of Results Title : Visualizing Gene Expression Results Content : Plotting heatmaps and PCA for visualizing data patterns. Example code for heatmap:r Copy code library( pheatmap ) pheatmap (assay( vst ( dds ))[ top_genes , ]) Example code for PCA:r Copy code plotPCA ( vst ( dds ), intgroup = "condition") Visual : Heatmap and PCA plot showing gene clustering and sample separation.