About Transcriptomics: From Bulk RNA-Seq to Single-Cell and Spatial Atlases

You will learn how a cell's RNA is converted into sequencing libraries, how those reads become a quantified expression matrix, and how that matrix is turned into biological claims. Starting from library preparation and spliced alignment, you move through count normalization, negative-binomial differential expression and pathway enrichment, then into single-cell droplet barcoding, quality control, graph-based clustering, cell-type annotation and dataset integration. The path finishes with dynamic and positional views of the transcriptome: pseudotime trajectories, RNA velocity, spatially resolved transcriptomics, and long-read isoform sequencing. By the end you can read a transcriptomics methods section critically and know which artifact each step introduces.