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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.
Rna-seq
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A step-by-step practical guide to performing differential gene expression analysis in R using DESeq2. Learn how to load counts, run the analysis, and interpret your results table.
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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.
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Bulk RNA-seq tells you the average gene expression of millions of cells at once. Single-cell RNA-seq tells you what every individual cell is doing. Here is how the technology works, how the data is analyzed, and how to know if you need it.
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Raw log2 CPM values are hard to interpret as a table. A heatmap fixes that — but the defaults are ugly. Here is how to build a publication-ready gene expression heatmap in R with pheatmap, including clustering, color palettes, and sample annotations.