Impeller: A Path-based Heterogeneous Graph Learning Method for Spatial Transcriptomic Data Imputation

Publication Date: 5/28/2024

Event: Bioinformatics

Reference: 40(6): 1-10, 2024

Authors: Ziheng Duan, University of California; Dylan Riffle, University of California; Ren Li, University of California; Junhao Liu, University of California; Martin Renqiang Min, NEC Laboratories America, Inc.; Jing Zhang, University of California

Abstract: Recent advances in spatial transcriptomics allow spatially resolved gene expression measurements with cellular or even sub-cellular resolution, directly characterizing the complex spatiotemporal gene expression landscape and cell-to-cell interactions in their native microenvironments. Due to technology limitations, most spatial transcriptomic technologies still yield incomplete expression measurements with excessive missing values. Therefore, gene imputation is critical to filling in missing data, enhancing resolution, and improving overall interpretability. However, existing methods either require additional matched single-cell RNA-seq data, which is rarely available, or ignore spatial proximity or expression similarity information

Publication Link: https://academic.oup.com/bioinformatics/article/40/6/btae339/7684233