Reference

Learning path and glossary

A staged plan for becoming fluent in omics analysis, followed by the vocabulary you will meet in papers and pipeline documentation.

Stages

Stage	Biology	Computation	Statistics	Practice project
1 · Foundations	Chapters 1–4 of this guide; a molecular biology textbook (Alberts, Molecular Biology of the Cell)	Linux shell, file formats, git; R basics (tidyverse) or Python (pandas)	Distributions, hypothesis tests, regression, p-values vs effect sizes	Download a GEO count matrix, make PCA and a volcano plot
2 · One layer well	Gene regulation; the biology of your system	Run nf-core/rnaseq; Bioconductor (SummarizedExperiment) or scverse (AnnData)	GLMs, negative binomial, multiple testing, empirical Bayes	Reanalyse a published RNA-seq study end to end and compare with the paper
3 · Single-cell and a second layer	Cell types, development, chromatin	Scanpy or Seurat; ATAC or proteomics pipelines; HPC job schedulers	Dimensionality reduction, clustering, mixed models, compositional data	Integrate two scRNA-seq datasets and test pseudobulk DE
4 · Integration and genetics	Population genetics, QTLs	PLINK, tensorQTL, MOFA+, mixOmics	Latent factor models, Bayesian inference, causal inference (MR, colocalisation)	Colocalise a GWAS with GTEx eQTLs; run MOFA+ on TCGA multi-omics
5 · Methods development	Deep knowledge of one system	PyTorch/JAX, package development, testing, benchmarking frameworks	Probabilistic modelling, variational inference, ML evaluation	Build and benchmark a method against neutral baselines