(A) Unsupervised hierarchical clustering can reveal novel molecular phenotypes which may inform endotype-based therapeutics. Gene expression signatures can also be used to derive biomarkers through supervised analyses. (B) Unsupervised gene co-expression networks can capture disease-associated changes in the network topology that may remain undetected through evaluation of gene expression levels alone. The use of both prior knowledge-based and data-driven bioinformatic tools can provide mechanistic insights into observed transcriptional changes (C), and offer a personalised view of the transcriptome in terms of both specific biological pathways, and network topology (D). (E) Single-cell RNA-sequencing provides opportunities for discovery through the identification of novel cell subsets, analysis of transitional states, and the study of cell–cell communication through ligand–receptor signalling. URA, Upstream Regulator Analysis; ChEA3, ChIP-X Enrichment Analysis 3; CARNIVAL, CAusal Reasoning pipeline for Network identification using Integer VALue programming; VIPER, Virtual Inference of Protein-activity by Enriched Regulon analysis; ARACNE, Algorithm for the Reconstruction of Accurate Cellular Networks; ssGSEA, single-sample Gene Set Enrichment Analysis; LIONESS, Linear Interpolation to Obtain Network Estimates for Single Samples.