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An honest head-to-head comparison of DESeq2 and edgeR. Learn the differences in their normalization, statistical tests, and run-times, and see if they actually give different biological answers.
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- Published on
A practical guide to designing a cost-effective and accurate amplicon next-generation sequencing (NGS) strategy to quantify CRISPR editing efficiency and characterises indels.- Published on
Run DESeq2 on a real RNA-seq experiment, start to finish: load the airway dataset, filter low counts, model paired donors, and read the results table without fooling yourself.- Published on
Before you run DESeq2, you need to understand what those numbers in your count matrix actually represent. This post explains what RNA-seq count data is, why raw counts are misleading, and exactly what format DESeq2 expects.- Published on
We’ve covered downloading data, normalization, and visualization. Now, we put it all together. This capstone post walks through a complete end-to-end analysis of a public breast cancer dataset (GSE183947) — from raw GEO download to identifying differentially expressed genes and creating a publication-ready volcano plot.
