Bi-Channel Masked Graph Autoencoders for Spatially Resolved Single-Cell Transcriptomics Data Imputation

Abstract

Spatially resolved transcriptomics bring exciting breakthroughs to single-cell analysis by providing physical locations along with gene expression. However, as a cost of the extremely high resolution, the technology also results in much more missing values in the data, i.e. dropouts. While a common solution is to perform imputation on the missing values, existing imputation methods majorly focus on transcriptomics data and tend to yield sub-optimal performance on spatial transcriptomics data. To advance spatial transcriptomics imputation, we propose a new technique to adaptively exploit the spatial information of cells and the heterogeneity among different types of cells. Furthermore, we adopt a mask-then-predict paradigm to explicitly model the recovery of dropouts and enhance the denoising effect. Compared to previous studies, our work focus on new large-scale cell-level data instead of spots or beads. Preliminary results have demonstrated that our method outperforms previous methods for removing dropouts in high-resolution spatially resolved transcriptomics data.