Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network.

Abstract

The integration of single-cell RNA sequencing (scRNA-seq) data from multiple experimental batches enables more comprehensive characterizations of cell states. Given that existing methods disregard the structural information between cells and genes, we proposed a structure-preserved scRNA-seq data integration approach using heterogeneous graph neural network (scHetG). By establishing a heterogeneous graph that represents the interactions between multiple batches of cells and genes, and combining a heterogeneous graph neural network with contrastive learning, scHetG concurrently obtained cell and gene embeddings with structural information. A comprehensive assessment covering different species, tissues and scales indicated that scHetG is an efficacious method for eliminating batch effects while preserving the structural information of cells and genes, including batch-specific cell types and cell-type specific gene co-expression patterns.