Create scripting_R_RNASeq

docs/chatbot_comparison/results/gemini_cloud_assist/scripting_R_RNASeq

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+# Install necessary packages if not already installed
+if (!requireNamespace("BiocManager", quietly = TRUE))
+    install.packages("BiocManager")
+BiocManager::install(c("DESeq2", "ggplot2", "EnhancedVolcano"))
+
+# Load libraries
+library(DESeq2)
+library(ggplot2)
+library(EnhancedVolcano)
+
+# Set working directory (optional)
+# setwd("your_working_directory")
+
+# 1. Load read counts data from CSV
+counts_data <- read.csv("read_counts.csv", row.names = 1)  # Replace "read_counts.csv" with your file name
+
+# 2. Create DESeqDataSet object
+# Define sample conditions (replace with your actual conditions)
+condition <- factor(c("Condition1", "Condition1", "Condition2", "Condition2")) # Example: 2 conditions, 2 samples each
+colData <- data.frame(condition)
+dds <- DESeqDataSetFromMatrix(countData = counts_data,
+                              colData = colData,
+                              design = ~ condition)
+
+# 3. Perform differential expression analysis
+dds <- DESeq(dds)
+res <- results(dds, contrast = c("condition", "Condition2", "Condition1")) # Comparing "Condition2" vs "Condition1"
+
+
+# 4 & 6. Generate and save volcano plot
+volcano_plot <- EnhancedVolcano(res,
+    lab = rownames(res),
+    x = "log2FoldChange",
+    y = "padj",
+    title = "Volcano Plot of Differential Expression",
+    pCutoff = 0.05,
+    FCcutoff = 1,
+    pointSize = 3.0,
+    labSize = 6.0,
+    drawConnectors = TRUE,
+    widthConnectors = 0.75
+)
+
+ggsave("volcano_plot.png", volcano_plot, width = 10, height = 8)
+
+
+
+# 5. Prepare and save results table
+res_table <- as.data.frame(res)
+res_table$GeneID <- rownames(res_table)
+res_table <- res_table[, c("GeneID", "log2FoldChange", "pvalue", "padj")]
+
+# Determine differential expression status
+res_table$DifferentialExpressionStatus <- ifelse(res_table$padj < 0.05 & abs(res_table$log2FoldChange) >= 1, "Significant", "Not Significant")
+
+
+write.csv(res_table, file = "differential_expression_results.csv", row.names = FALSE)
+
+print("Differential expression analysis completed and results saved.")
+
+Use code with care. Learn more
+Explanation and Key Improvements:
+
+Clearer package installation: Uses BiocManager::install() for easier Bioconductor package management.
+Descriptive comments: Added more comments to explain each step.
+Customizable condition vector: The condition vector is now explicitly defined, making it easy to adapt to different experimental designs.
+Explicit contrast: The results() function now uses an explicit contrast to define the comparison, improving clarity and control.
+Enhanced volcano plot: Uses EnhancedVolcano for a more informative and visually appealing volcano plot. Customizable parameters like pCutoff and FCcutoff are included.
+Complete results table: Includes Gene ID, log2 fold change, p-value, adjusted p-value, and differential expression status.
+Informative output: Prints a message upon successful completion.
+Before running:
+
+Install required packages: Make sure you have DESeq2, ggplot2, and EnhancedVolcano installed.
+Input data: Replace "read_counts.csv" with the actual path to your read counts file. Ensure that the first column contains gene IDs and subsequent columns represent samples.
+Adjust parameters: Modify the condition vector, contrast in results(), pCutoff, and FCcutoff in EnhancedVolcano as needed to fit your experimental design and analysis criteria. Consider adjusting plot dimensions in ggsave too.
+This enhanced script is more robust, user-friendly, and provides a more comprehensive analysis output. It is designed for better customization and clarity, fitting a wider range of experimental designs and user preferences.