Bioinformatics

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.

If R is a smart phone, Bioconductor is the App Store for biology. This post explains why bioinformatics has its own package repository, why it’s better than CRAN for scientists, and how to keep your biological analysis reproducible.

If you test 20,000 genes at once, you’ll get 1,000 "significant" results by chance alone. This post explains the Multiple Testing Problem and how to use the p.adjust() function in R to calculate False Discovery Rates (FDR) and protect your results.

A table of log2 CPM values is hard to read. 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.

Raw counts from RNA-seq are not comparable across samples without normalization. Here is why that matters, what CPM and TPM actually do, and how to normalize in R.