聚类分析
计算机科学
自编码
图形
推论
降噪
数据挖掘
可视化
人工智能
模式识别(心理学)
卷积(计算机科学)
深度学习
理论计算机科学
人工神经网络
作者
Haiyun Wang,Jianping Zhao,Yansen Su,Chun-Hou Zheng
标识
DOI:10.1109/tcbb.2021.3126641
摘要
Identifying cell types is one of the main goals of single-cell RNA sequencing (scRNA-seq) analysis, and clustering is a common method for this item. However, the massive amount of data and the excess noise level bring challenge for single cell clustering. To address this challenge, in this paper, we introduced a novel method named single-cell clustering based on denoising autoencoder and graph convolution network (scCDG), which consists of two core models. The first model is a denoising autoencoder (DAE) used to fit the data distribution for data denoising. The second model is a graph autoencoder using graph convolution network (GCN), which projects the data into a low-dimensional space (compressed) preserving topological structure information and feature information in scRNA-seq data simultaneously. Extensive analysis on seven real scRNA-seq datasets demonstrate that scCDG outperforms state-of-the-art methods in some research sub-fields, including single cell clustering, visualization of transcriptome landscape, and trajectory inference.
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