Extrapolating Heterogeneous Time-Series Gene Expression Data using Sagittarius

Abstract

Understanding the temporal dynamics of gene expression is crucial for developmental biology, tumor biology, and biogerontology. However, some time points remain challenging to measure in the lab, particularly during very early or very late stages in a biological process. Here we propose Sagittarius, a transformer-based model that is able to accurately simulate gene expression profiles at time points outside of the range of times measured in the lab. The key idea behind Sagittarius is to learn a shared reference space that generates simulated time series measurements, thereby explicitly modeling unaligned time points and conditional batch effects between time series and making the model widely applicable to diverse biological settings. We show the promising performance of Sagittarius when extrapolating mammalian developmental gene expression, simulating drug-induced expression at unmeasured dose and treatment times, and augmenting datasets to accurately predict drug sensitivity. We also used Sagittarius to simulate mutation profiles for early-stage cancer patients, which further enabled us to discover a gene set related to the Hedgehog signaling pathway that may be related to tumorigenesis in sarcoma patients, including PTCH1, ARID2, and MYCBP2. By augmenting experimental temporal datasets with crucial but difficult-to-measure simulated datapoints, Sagittarius enables deeper insights into the temporal dynamics of heterogeneous transcriptomic processes and can be broadly applied to biological time series extrapolation.