A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings
Hongru Shen, Xilin Shen, Mengyao Feng, Dan Wu, Chao Zhang, Yichen Yang, Meng Yang, Jiani Hu, Jilei Liu, Wei Wang, Li Yang, Qiang Zhang, Jilong Yang, Kexin Chen, Xiangchun Li
Abstract
Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulations of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 11.9 million single cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene-gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.