genomics-analysislisted

# Genomics Analysis in R ## Overview Comprehensive genomics and bioinformatics statistical methods using Bioconductor packages. Covers differential expression analysis, pathway enrichment, and visualization for RNA-seq and microarray data. ## Bioconductor Setup ```r # Install Bioconductor if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") # Install packages BiocManager::install(c( "DESeq2", "edgeR", "limma", "clusterProfiler", "org.Hs.eg.db", "EnhancedVolcano", "ComplexHeatmap" )) ``` ## RNA-seq Differential Expression ### DESeq2 Analysis ```r library(DESeq2) # Create DESeqDataSet from count matrix dds <- DESeqDataSetFromMatrix( countData = count_matrix, colData = sample_info, design = ~ condition ) # Filter low counts keep <- rowSums(counts(dds) >= 10) >= min_samples dds <- dds[keep, ] # Run DESeq2 dds <- DESeq(dds) # Get results res <- results(dds, contrast = c("condition", "treatment", "control")) # Shrink log fold changes (for visualization) res_shrunk <- lfcShrink(dds, coef = "condition_treatment_vs_control", type = "apeglm") # Summary summary(res) # Significant genes sig_genes <- subset(res, padj < 0.05 & abs(log2FoldChange) > 1) ``` ### DESeq2 with Multiple Factors ```r # Multi-factor design dds <- DESeqDataSetFromMatrix( countData = count_matrix, colData = sample_info, design = ~ batch + condition # Control for batch ) dds <- DESeq(dds) # Results controlling for batch res <- results(dds, contr