A Hybrid Algorithm based on Autoencoder and Text Convolutional Network for Integrating scATAC-seq and scRNA-seq Data

Xiaoshu Zhu, Jiahui Liu, Wei Lan, Fei Teng

Abstract: Integrating single-cell RNA-seq (scRNA-seq) data and single-cell ATAC-seq (scATAC-seq) data provides a more comprehensive view of cellular heterogeneity. However, the high sparsity in scATAC-seq data presents significant challenges for cell type identification, while scRNA-seq offers richer gene expression information for more accurate annotation. We propose scEDI, a method that integrates scRNA-seq and scATAC- seq data using autoencoders and text convolutional networks. In scEDI, both data types are processed through an autoencoder to create a shared low-dimensional space, followed by a text convolutional network for label transfer. We evaluated scEDI against three state-of-the-art methods on datasets from the adult mouse cerebral cortex and human peripheral blood monocytes. Experimental results demonstrate that scEDI improves label transfer accuracy, particularly in small datasets.