Differential Gene Expression Analysis in R with DESeq2| Bioinformatics Tutorial for Beginners

Описание: Differential Gene Expression Analysis in R with DESeq | Bioinformatics for Beginners| Bioinformatics Tutorial| Gene Expression Analysis using Deseq2

Description:
Welcome to our comprehensive tutorial on performing differential gene expression analysis using R and DESeq2 for RNA sequencing data! In this video, we'll guide you through the entire process, from data preprocessing to advanced visualization techniques.

🔍 Here's what you'll learn in this tutorial:
1️⃣ Data Import and Preprocessing: We'll start by loading your RNA-seq data into R and performing essential data preprocessing steps, ensuring that your data is ready for analysis.

2️⃣ MA Plot: You'll discover how to create MA plots, a valuable tool for visualizing the distribution of gene expression values and identifying differentially expressed genes.

3️⃣ Dispersion Plot: We'll dive into dispersion plots to assess the variability of gene expression across samples and ensure the reliability of your analysis.

4️⃣ Principal Component Analysis (PCA): Learn how to perform PCA to reduce the dimensionality of your data and uncover underlying patterns, helping you identify potential outliers and clusters.

5️⃣ Volcano Plot: Uncover the secrets of volcano plots, a powerful visualization method for highlighting significantly differentially expressed genes while controlling for false positives.

6️⃣ Heatmap Plot: Finally, we'll create heatmaps to visualize gene expression patterns across samples and clusters, providing insights into gene co-expression and functional enrichment.

Whether you're new to RNA-seq analysis or looking to enhance your skills, this video will equip you with the knowledge and tools you need to effectively analyze and visualize your RNA sequencing data with DESeq2 in R.

Don't forget to like, subscribe, and hit the notification bell to stay updated with more exciting data analysis tutorials and tips. Let's dive into the world of differential gene expression analysis together! 🧬📊📈

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• DESeq2 Vignette: http://bioconductor.org/packages/deve...

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