Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Variational Autoencoder Based Dimension Reduction and Clustering for Single-Cell RNA-seq Gene Expression

단일세포 RNA-SEQ의 유전자 발현 군집화를 위한 변이 자동인코더 기반의 차원감소와 군집화

  • Chi, Sang-Mun (Department of Computer Science, Kyungsung University)
  • Received : 2021.08.22
  • Accepted : 2021.09.15
  • Published : 2021.11.30
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Abstract

Since single cell RNA sequencing provides the expression profiles of individual cells, it provides higher cellular differential resolution than traditional bulk RNA sequencing. Using these single cell RNA sequencing data, clustering analysis is generally conducted to find cell types and understand high level biological processes. In order to effectively process the high-dimensional single cell RNA sequencing data fir the clustering analysis, this paper uses a variational autoencoder to transform a high dimensional data space into a lower dimensional latent space, expecting to produce a latent space that can give more accurate clustering results. By clustering the features in the transformed latent space, we compare the performance of various classical clustering methods for single cell RNA sequencing data. Experimental results demonstrate that the proposed framework outperforms many state-of-the-art methods under various clustering performance metrics.

단일세포 RNA-Seq 은 개별 세포의 유전자 발현을 제공하므로 세포마다 차등적인 고해상도 정보를 준다. 단일세포 RNA-Seq 자료에 대하여 군집화는 세포의 유형과 고수준의 생물 과정을 이해하기 위하여 수행된다. 매우 고차원이고 대용량인 단일세포 RNA-Seq을 효과적으로 처리하기 위하여, 본 논문은 변이 자동인코더를 사용하여 고차원의 자료공간을 저차원의 잠재공간으로 변환하여, 보다 정확한 군집화를 수행할 수 있는 특징공간을 만든다. 차원이 축소된 잠재공간에 다양한 군집화 방법을 적용하는 접근을 다양한 전통적인 단일세포 RNA-Seq 군집화 방법과 성능을 비교하였다. 군집화 실험을 통하여, 제안한 방법은 기존 방법들보다 다양한 군집화 성능기준에서 성능이 개선되었다.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2021R1I1A304651111)

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