Tag: scanpy

A working guide to clustering bulk and single-cell RNA-seq: normalization choices that decide your dendrogram, gene-module discovery across timepoints, choosing k defensibly, and the failure modes that produce confident nonsense.

A working guide to going from FASTQ or a count matrix to a UMAP embedding you can defend: normalization, PCA, neighbor graph settings, initialization, and what the picture does and does not tell you.