22 We present DeepMAPS (Deep learning-based Multi-omics Analysis Platform for Single-23 cell data) for biological network inference from single-cell multi-omics (scMulti-omics). 24 DeepMAPS includes both cells and genes in a heterogeneous graph to simultaneously 25 infer cell-cell, cell-gene, and gene-gene relations. The multi-head attention mechanism in 26 a graph transformer considers the heterogeneous relation among cells and genes within 27 both local and global context, making DeepMAPS robust to data noise and scale. We 28 benchmarked DeepMAPS on 18 scMulti-omics datasets for cell clustering and biological 29 network inference, and the results showed that our method outperformed various existing 30 tools. We further applied DeepMAPS on lung tumor leukocyte CITE-seq data and matched 31 diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In both cases, 32 DeepMAPS showed competitive performance in cell clustering and predicted biologically 33 meaningful cell-cell communication pathways based on the inferred gene networks. Note 34 that we deployed a webserver using DeepMAPS implementation equipped with multiple 35 functions and visualizations to improve the feasibility and reproducibility of scMulti-omics 36 data analysis. Overall, DeepMAPS represents a heterogeneous graph transformer for 37 single-cell study and may benefit the use of scMulti-omics data in various biological 38 systems. 39
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