How Does DESeq2 Work? | Bioinformatics for Beginners | Differential Gene Expression Analysis Theory

Welcome to my bioinformatics video where we dive deep into the inner workings of DESeq2, the ultimate tool for RNA-Seq data analysis. In this comprehensive guide, we uncover the secrets behind DESeq2's powerful algorithms and how it tackles various sources of variation in count data.
Key Elements Inside the Video:
Normalization Demystified: Unravel the concept of normalization, addressing library size variation and RNA composition variation using negative binomial distribution.
Conquering Library Size Variation: Discover how DESeq2 handles library size variation, leveling the playing field by scaling read counts to account for differences in sequencing depth.
RNA Composition Bias Unveiled: Explore DESeq2's approach to RNA composition bias, ensuring precise differential expression analysis by accounting for variations in transcript abundance.
Navigating Sequencing Depth Variation: Uncover how DESeq2 tackles sequencing depth variation through sophisticated statistical modeling, ensuring reliable gene expression estimates.
Harnessing the Power of Negative Binomial Distribution: Understand why DESeq2 relies on the negative binomial distribution, providing a flexible framework to model count data variability with precision.
Log2 (qij) = xj Bi: Explore the equation governing gene expression level changes and its role in DESeq2.
Upregulation and Downregulation: Understand how DESeq2 identifies and distinguishes between gene upregulation and downregulation.
Statistical Testing Strategies: Dive into DESeq2's statistical testing prowess, including size factor estimation, dispersion estimation, calculation of test statistics such as the Wald test, ensuring robust analysis outcomes.
Precision in P-Values: Explore how DESeq2 calculates p-values and adjusts them for multiple testing, ensuring confidence in the identification of differentially expressed genes.
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The link for our video about Differential Gene Expression Analysis in R with Deseq2:
• Differential Gene Expr...
#DESeq2 #RNASeq #Bioinformatics #Genomics #DifferentialExpression #mrbioinformatix #dna #dnaanalysis #geneexpression
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0:00 Introduction
1:31 Library Size Variation
3:51 RNA Composition Variation
4:35 Sequencing Depth Variation
7:59 Negative Binomial Distribution
14:45 Size Factor Estimation
17:59 Estimating Dispersion
23:24 Calculation of Test Statistic
24:42 Statistical Significance Analysis