#RCoding
Ever wondered how scientists identify genes that change between conditions? Identifying these differentially expressed genes is crucial in understanding biological processes.
Here's where R programming comes into play! ✨ We can use DESeq2 to analyze RNA-Seq data and visualize DEGs with two powerful plots:
𝟏. 𝐌𝐀 𝐏𝐥𝐨𝐭: This plot reveals the relationship between the average expression level (M) and the fold change (A) of a gene. Genes with high fold changes (up or down) tend to be further away from zero on the y-axis.
𝟐. 𝐕𝐨𝐥𝐜𝐚𝐧𝐨 𝐏𝐥𝐨𝐭: This plot highlights both fold change (x-axis) and statistical significance (y-axis, typically -log10(p-value)). Genes with a significant change (low p-value) and a large fold change (positive or negative) stand out like volcanic peaks!
Both plots are crucial for identifying differentially expressed genes (DEGs) in RNA-Seq data analysis.
𝐖𝐚𝐧𝐭 𝐭𝐨 𝐦𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐞𝐬𝐞 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐚𝐧𝐝 𝐮𝐧𝐥𝐨𝐜𝐤 𝐭𝐡𝐞 𝐩𝐨𝐰𝐞𝐫 𝐨𝐟 𝐑 𝐟𝐨𝐫 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬?
Enroll in our upcoming workshop on 𝐑𝐍𝐀-𝐒𝐞𝐪 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐮𝐬𝐢𝐧𝐠 𝐑 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠! We'll guide you through DESeq2 analysis, generating stunning visualizations like these, and empower you to explore your own RNA-Seq data.
🔗 Register here: https://forms.gle/zzMPZ1rHzghNrDMw7
📅 Date: July 03 - July 08, 2024
🕒 Time: 7:00 PM IST | 8:30 AM CST
📍 Location: Online
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#R #RNASeq #Transcriptomics #Programming #Coding #Bioinformatics #DataScience #Workshop #OnlineTraining #Certification #DataAnalysis #RStudio #DataVisualization #ggplot2 #StatisticalAnalysis #Bioconductor #Limma #DESeq2 #clusterProfiler #PathwayAnalysis #RNASeq #NGS #Networking #Technology
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