Generation of synthetic scRNA-seq-like transcriptomes using a generative adversarial network from RNA-seq data

Next-generation sequencing (NGS) technologies have become integral for high-throughput transcriptomic studies. Among these, single-cell RNA sequencing (scRNA-seq) is especially valuable for quantifying gene expression at the individual cell level, enabling the identification of rare cell populations and cellular differentiation pathways. However, the high cost of scRNA-seq often limits its broader application. Bulk RNA sequencing (RNA-seq) provides a more affordable alternative but lacks the single-cell resolution needed to elucidate cellular heterogeneity. Here, we present a cycle-consistent generative adversarial network (cycleGAN) approach to generate synthetic single-cell-like transcriptomes from bulk RNA-seq data. By adversarially training two sets of generators and discriminators, our framework attempts to learn the relationship between bulk and single-cell transcriptome distributions. Although this approach does not replace real scRNA-seq experiments, it can be a usefult tool to generate synthetic single-cell-like data for preliminary exploratory investigations and other machine learning applications. We further discuss the performance, limitations, and ethical considerations of our method.