Generative Reconstruction of Unobserved Cellular Dynamics using Single-Cell Transcriptomic Trajectories

ABSTRACT The primary challenge in cellular dynamics is to capture the intermediate states between two distinct biological stages. This is because of the technical constraints in capturing transient states or the nature of single-cell sequencing protocols. To address this challenge, we introduce a new computational framework, GRAIL ( G enerative R econstruction of A rtificial I ntermediate L ineages), which aims to bridge these missing transitions. GRAIL reconstructs biologically plausible intermediate cell states between two distinct developmental stages (A and B) through a Locality Sensitive Hashing (LSH) based cell selection strategy followed by a smooth interpolation in learned latent space. The interpolated latent representations are subsequently decoded into gene expression profiles using a trained generator from a Generative Adversarial Network (GAN). The framework consists of three components: (a) a pretrained autoencoder that learns latent representations of stage specific transcriptomes, (b) an LSH-guided interpolation procedure that identifies anchor cells and performed interpolation in latent space, and (c) a GAN generator that extrapolates realistic intermediate expression from the interpolated samples consistent with the underlying trajectory. We benchmarked GRAIL with different state-of-the-arts (SOTA) in diverse setup of simulated dataets as well as in real life scRNA-seq datasets. Generated samples from GRAIL preserve expected marker gene expression patterns and also improve downstream analyses, including differential expression and cell clustering. Our method addresses the critical gap in studying cellular transitions when experimental intermediate samples are unavailable.