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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 4: AAAI-22 Technical Tracks 4

ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations

February 1, 2023

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Authors

Zhuohan Yu

Jilin University


Yifu Lu

Jilin University


Yunhe Wang

Hebei University of Technology


Fan Tang

Jilin University


Ka-Chun Wong

City University of Hong Kong


Xiangtao Li

Jilin University


DOI:

10.1609/aaai.v36i4.20392


Abstract:

Single-cell RNA sequencing (scRNA-seq) provides high-throughput information about the genome-wide gene expression levels at the single-cell resolution, bringing a precise understanding on the transcriptome of individual cells. Unfortunately, the rapidly growing scRNA-seq data and the prevalence of dropout events pose substantial challenges for cell type annotation. Here, we propose a single-cell model-based deep graph embedding clustering (scTAG) method, which simultaneously learns cell–cell topology representations and identifies cell clusters based on deep graph convolutional network. scTAG integrates the zero-inflated negative binomial (ZINB) model into a topology adaptive graph convolutional autoencoder to learn the low-dimensional latent representation and adopts Kullback–Leibler (KL) divergence for the clustering tasks. By simultaneously optimizing the clustering loss, ZINB loss, and the cell graph reconstruction loss, scTAG jointly optimizes cluster label assignment and feature learning with the topological structures preserved in an end-to-end manner. Extensive experiments on 16 single-cell RNA-seq datasets from diverse yet representative single-cell sequencing platforms demonstrate the superiority of scTAG over various state-of-the-art clustering methods.

Topics: AAAI

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HOW TO CITE:

Zhuohan Yu||Yifu Lu||Yunhe Wang||Fan Tang||Ka-Chun Wong||Xiangtao Li ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations Proceedings of the AAAI Conference on Artificial Intelligence (2022) 4671-4679.

Zhuohan Yu||Yifu Lu||Yunhe Wang||Fan Tang||Ka-Chun Wong||Xiangtao Li ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations AAAI 2022, 4671-4679.

Zhuohan Yu||Yifu Lu||Yunhe Wang||Fan Tang||Ka-Chun Wong||Xiangtao Li (2022). ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations. Proceedings of the AAAI Conference on Artificial Intelligence, 4671-4679.

Zhuohan Yu||Yifu Lu||Yunhe Wang||Fan Tang||Ka-Chun Wong||Xiangtao Li. ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.4671-4679.

Zhuohan Yu||Yifu Lu||Yunhe Wang||Fan Tang||Ka-Chun Wong||Xiangtao Li. 2022. ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations. "Proceedings of the AAAI Conference on Artificial Intelligence". 4671-4679.

Zhuohan Yu||Yifu Lu||Yunhe Wang||Fan Tang||Ka-Chun Wong||Xiangtao Li. (2022) "ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations", Proceedings of the AAAI Conference on Artificial Intelligence, p.4671-4679

Zhuohan Yu||Yifu Lu||Yunhe Wang||Fan Tang||Ka-Chun Wong||Xiangtao Li, "ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations", AAAI, p.4671-4679, 2022.

Zhuohan Yu||Yifu Lu||Yunhe Wang||Fan Tang||Ka-Chun Wong||Xiangtao Li. "ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.4671-4679.

Zhuohan Yu||Yifu Lu||Yunhe Wang||Fan Tang||Ka-Chun Wong||Xiangtao Li. "ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 4671-4679.

Zhuohan Yu||Yifu Lu||Yunhe Wang||Fan Tang||Ka-Chun Wong||Xiangtao Li. ZINB-Based Graph Embedding Autoencoder for Single-Cell RNA-Seq Interpretations. AAAI[Internet]. 2022[cited 2023]; 4671-4679.


ISSN: 2374-3468


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Copyright 2022, Association for the Advancement of
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