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

Shen, H.; Shen, X.; Feng, M.; Wu, D.; Zhang, C.; Yang, Y.; Yang, M.; Hu, J.; Liu, J.; Wang, W.; Li, Y.; Zhang, Q.; Yang, J.; Chen, K.; Li, X.

doi:10.1101/2021.08.23.457305

bioRxiv · 10.1101/2021.08.23.457305

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

Shen, H.·Shen, X.·Feng, M.·Wu, D.·Zhang, C.·Yang, Y.·Yang, M.·Hu, J.·Liu, J.·Wang, W.·Li, Y.·Zhang, Q.·Yang, J.·Chen, K.·Li, X.

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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 accumulation 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 13.7 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.

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Shen, H., Shen, X., Feng, M., Wu, D., Zhang, C., Yang, Y., Yang, M., Hu, J., Liu, J., Wang, W., Li, Y., Zhang, Q., Yang, J., Chen, K., Li, X.. 2021-08-24. A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings. https://doi.org/10.1101/2021.08.23.457305

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