Author

Hongru Shen

Tianjin Medical University Cancer Institute and HospitalORCID 0000-0001-9147-6044Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:scRNA-seq· 3 Research Focus:batch correction / integration· 2 Research Focus:clustering· 2 Approach:statistical model· 2 Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Jiani HuiD· 3 shared Jilei LiuiD· 3 shared Kexin CheniD· 3 shared Meng YangiD· 3 shared Mengyao Feng· 3 shared Xiangchun LiiD· 3 shared Xilin SheniD· 3 shared Yichen YangiD· 3 shared

Activity by Year

2021-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersBioinformatics

Jiani HuORCID

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Author profile →

3 papers

Recent·Most cited

Scalable batch-correction method for integrating large-scale single-cell transcriptomes

Xilin Shen, Hongru Shen, Dan Wu, Mengyao Feng, Jiani Hu, Jilei Liu, Yichen Yang, Meng Yang, Yang Li, Lei Shi, Kexin Chen, Xiangchun Li

This paper introduces a scalable batch-correction method for integrating large-scale single-cell transcriptomes and shows it matches state-of-the-art performance on 18 million cells from the Human Cell Atlas.

Briefings in Bioinformatics · Aug 10, 2022Peer-reviewedBioinformatics

Generative pretraining from large-scale transcriptomes: Implications for single-cell deciphering and clinical translation

Hongru Shen, Xilin Shen, Jiani Hu, Jilei Liu, Chao Zhang, Dan Wu, Mengyao Feng, Meng Yang, Yang Li, Yichen Yang, Wei Wang, Qiang Zhang, Jilong Yang, Kexin Chen, Xiangchun Li

The paper introduces tGPT, a generative model for integrating large-scale single-cell transcriptomes, showing its effectiveness in clustering, trajectory inference, and linking gene patterns to clinical outcomes.

bioRxiv · Jan 31, 2022 · PreprintBioinformatics

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

The paper introduces iSEEEK, a method for integrating large-scale single-cell transcriptomes using gene rankings, showing good clustering and transferability across datasets.

Briefings in Bioinformatics · Dec 14, 2021Peer-reviewedBioinformatics