A comprehensive review article titled “Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses,” published in Quantitative Biology, offers a detailed, side-by-side comparison of bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) (Figure 1), providing researchers with a practical roadmap for selecting and applying these powerful tools across diverse biological contexts.

The review dissects the computational workflows, strengths, limitations, and future directions of both technologies. It emphasizes that bulk RNA-seq remains the most cost-effective and robust method for analyzing gene expression across large sample sizes—ideal for clinical cohorts, agricultural populations, and time-series studies. In contrast, scRNA-seq offers unprecedented resolution, enabling scientists to uncover cellular heterogeneity, identify rare cell types, and trace developmental trajectories at the single-cell level.

This review also highlights key computational considerations, including quality control, normalization, batch effect correction, and integration with multi-omics data. It warns against the blind application of pipelines, noting that technical artifacts—like dropout events in scRNA-seq or batch effects in bulk RNA-seq—can easily be mistaken for biological signals if not properly addressed.

Looking ahead, the authors emphasize the growing role of machine learning and deep learning in data denoising, dimensionality reduction, and trajectory inference. They also spotlight emerging integrative technologies—such as spatial transcriptomics and single-cell multi-omics—that combine gene expression with genomic, epigenetic, or proteomic data to reveal new layers of biological insight.
DOI: 10.1002/qub2.78