Tag: Differential Expression
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How to Analyze RNAseq Data for Absolute Beginners 21: A Comprehensive Guide to Batch Effects & Covariates Adjustment
Introduction to Batch Effects in RNA-seq Analysis In high-throughput sequencing experiments, batch effects represent one of the most challenging technical hurdles researchers face. These systematic variations arise not from biological differences between samples but from technical factors in the experimental process. Understanding and properly adjusting for batch effects is essential for generating reliable and reproducible…
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How to Analyze RNAseq Data for Absolute Beginners Part 20: Comparing limma, DESeq2, and edgeR in Differential Expression Analysis
Introduction Differential expression (DE) analysis represents a fundamental step in understanding how genes respond to different biological conditions. When we perform RNA sequencing, we’re essentially taking a snapshot of all the genes that are active (or expressed) in our samples at a given moment. However, the real biological insights come from understanding how these expression…
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How to Analyze RNAseq Data for Absolute Beginners Part 3: From Count Table to DEGs – Best Practices
Video Tutorial As we move forward in our RNAseq analysis journey, we’ll be transitioning from the Linux environment to R, a powerful and versatile statistical analysis tool. R is not only a programming language but also a platform widely used in data science, statistical computing, and predictive modeling. Tech giants like Microsoft, Meta, Google, Amazon,…
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Alternative Splicing Analysis Cancer ChIP-seq deepTools DESeq2 Differential Expression edgeR FPKM Fusion Gene Detection gene expression HPC Isoform limma microRNA miRNA miRNA-seq Normalization peak visualization profile plot Quantification RNAseq analysis Salmon smRNA smRNA-seq TPM Transcript UMI Virus Virus-Host Virus Gene Expression