#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
🔄 Share this post with your colleagues who might be interested!
#R #RNASeq #Transcriptomics #Programming #Coding #Bioinformatics #DataScience #Workshop #OnlineTraining #Certification #DataAnalysis #RStudio #DataVisualization #ggplot2 #StatisticalAnalysis #Bioconductor #Limma #DESeq2 #clusterProfiler #PathwayAnalysis #RNASeq #NGS #Networking #Technology
Watch video #RCoding online without registration, duration hours minute second in high quality. This video was added by user OmicsLogic 01 January 1970, don't forget to share it with your friends and acquaintances, it has been viewed on our site 228 once and liked it 19 people.