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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.

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings