scTIE: data integration and inference of gene regulation using single-cell temporal multimodal data

Abstract

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal datasets, we demonstrate scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome dataset we generated from differentiating mouse embryonic stem cells over time, we demonstrate scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

Differential developmental blueprints of organ-intrinsic nervous systems

“…The integrated ICNS and ENS dataset (ICNS, E12.5 – E18.5; ENS, E13.5 – E18.5) was used for scTIE analysis. As the scTIE was originally designed for paired single-cell RNA-sequencing and ATAC-seq data 48 , we first modified scTIE for single-cell RNA-sequencing data by removing the encoder and decoder for ATAC-seq and the modality alignment loss. We used the integrated matrix of 2000 highly variable genes returned by Seurat as input.…”

“…To better understand organ-intrinsic neuron fate selection, we further performed scTIE, a method that infers regulatory relationships predictive of cellular state changes from large single-cell temporal datasets 48 . Interestingly, scTIE results show that both ENS- and ICNS-precursors are possible of becoming ENS- or ICNS-neurons, and their fate probabilities gradually shift to their corresponding neuron types during neurogenesis (Fig.…”