A Meta-learning based Graph-Hierarchical Clustering Method for Single Cell RNA-Seq Data

Pan, Z.; Lin, Y.; Zhang, H.; Zeng, Y.; Yu, W.; Yang, Y.

doi:10.1101/2022.09.06.506784

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bioRxiv · 10.1101/2022.09.06.506784

A Meta-learning based Graph-Hierarchical Clustering Method for Single Cell RNA-Seq Data

Pan, Z.·Lin, Y.·Zhang, H.·Zeng, Y.·Yu, W.·Yang, Y.

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Abstract

Single cell sequencing techniques enable researchers view complex bio-tissues from a more precise perspective to identify cell types. However, more and more recent works have been done to find more detailed subtypes within already known cell types. Here, we present MeHi-SCC, a method which utilized meta-learning protocol and brought in multi scRNA-seq datasets information in order to assist graph-based hierarchical sub-clustering process. In result, MeHi-SCC outperformed current-prevailing scRNA clustering methods and successfully identified cell subtypes in two large scale cell atlas. Our codes and datasets are available online at https://github.com/biomed-AI/MeHi-SCC

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Pan, Z., Lin, Y., Zhang, H., Zeng, Y., Yu, W., Yang, Y.. 2022-09-08. A Meta-learning based Graph-Hierarchical Clustering Method for Single Cell RNA-Seq Data. https://doi.org/10.1101/2022.09.06.506784

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