01 / Reading Guide
Reading Guide
Who this book is for
For upper-level undergraduate and beginning graduate students entering genomic data analysis from biology, medicine or quantitative disciplines. The author explicitly recognizes these different backgrounds. The text is useful when readers want to connect a computational procedure to the assay and biological question that generated the data.
Prerequisites
Basic molecular biology and introductory statistics are helpful, but the book offers different starting routes. Biologists can begin with R; quantitative readers should first review genome organization, gene regulation and experimental measurement.
What it covers
Genome biology, high-throughput methods and R foundations lead into data handling, statistical exploration and genomic analysis workflows. The complete text includes examples across common genomic measurements and emphasizes interpretation as well as computation. Technical tools are introduced in relation to the scientific task rather than as a disconnected collection of commands.
How to use it
Choose a small dataset and record the assay, experimental unit, biological comparison and expected sources of variation before running an analysis. Preserve the raw input and write transformations in a reproducible script. Inspect data quality and sample relationships before interpreting a fitted result. When changing a filtering or normalization choice, explain which assumption changed and compare the consequences. Recreate one example with the supplied data, then state what additional checks would be needed for a dataset from another experiment.
Edition and sources
The complete author-hosted book is dated September 30, 2020 and licenses the online text under CC BY-NC-SA 4.0. Linked software and datasets can have separate conditions. This is the complete reading edition, not a publisher preview; later package versions may require small adaptations to example code.
