DTPSP: A Deep Learning Framework for Optimized Time Point Selection in Time-Series Single-Cell Studies

Abstract

Time-series studies are critical for uncovering dynamic biological processes, but achieving comprehensive profiling and resolution across multiple time points and modalities (multi-omics) remains challenging due to cost and scalability constraints. Current methods for studying temporal dynamics, whether at the bulk or single-cell level, often require extensive sampling, making it impractical to deeply profile all time points and modalities. To overcome these limitations, we present DTPSP, a deep learning framework designed to identify the most informative time points in any time-series study, enabling resource-efficient and targeted analyses. DTPSP models temporal gene expression patterns using readily obtainable data, such as bulk RNA-seq, to select time points that capture key system dynamics. It also integrates a deep generative module to infer data for non-sampled time points based on the selected time points, reconstructing the full temporal trajectory. This dual capability enables DTPSP to prioritize key time points for in-depth profiling, such as single-cell sequencing or multi-omics analyses, while filling gaps in the temporal landscape with high fidelity. We apply DTPSP to developmental and disease-associated time courses, demonstrating its ability to optimize experimental designs across bulk and single-cell studies. By reducing costs, enabling strategic multi-omics profiling, and enhancing biological insights, DTPSP provides a scalable and generalized solution for investigating dynamic systems.