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A basic task in the analysis of count data from RNA-seq is the detection of differentially expressed genes. DESeq2 is one of the most commonly used packages to perform differential gene expression analysis in R. In this video, I have tried to explained the DESeq2 model and provide some intuition on what goes behind this package and the steps performed to call differentially expressed genes. I have tried my best to keep it simple and explain it to the best of my knowledge. Please feel free to leave your comments below, I am happy to hear your thoughts, as well as any links to articles/blogs/papers that you think, explain these concepts better! Let's use this space to share resources and learn more! Here are some resources that helped me to understand some of these concepts: 1. #theory-behind-deseq2 2. 3. 4. 5. #:~:text=Step 1: DESeq2 creates a,less influenced by extreme values. Chapters 0:00 Intro 0:29 A typical study design 1:32 Features of RNA-Seq counts data 3:04 Poisson distribution for counts data 5:14 Why is Poisson not the best model? 6:58 Negative Binomial is the way to go! 8:46 DESeq2 steps 9:32 Biases in counts data 12:29 Estimate Size Factor (median of ratios method) 16:37 Estimate Dispersions 20:00 Generalized Linear Models 24:21 Hypothesis testing Show your support and encouragement by buying me a coffee: To get in touch: Website: Github: Email: khushbu_p@ #bioinformagician #bioinformatics #deseq2 #differentialgeneexpressionanalysis #rnaseq #fpkm #rpkm #tpm #normalization #rna #ncbi #genomics #beginners #tutorial #howto #omics #research #biology #ngs
