@inproceedings{867291db29104476988cf55ee8765cfe,
title = "ScSSC: Semi-supervised Single Cell Clustering Based on 2D Embedding",
abstract = "In recent years, with the development of single-cell RNA sequencing (scRNA-seq) technology, more and more scRNA-seq data has been generated. Corresponding analysis methods such as clustering analysis are also proposed, which effectively distinguish the cell types and reveal the cell diversity. However, due to more than ten thousand genes for normal species, the dimension of scRNA-seq data is very high. Meanwhile, there exist many zero counts in scRNA-seq data. They all increase the difficulty of clustering analysis of scRNA-seq data. This paper proposes ScSSC, a semi-supervised clustering method based on 2D embedding. ScSSC uses the autoencoder for pre-training to construct the network and applies the community discovery algorithm to label cells. Then a semi-supervised network is used to clustering the data after training. The clustering results of three public data sets show that ScSSC has better performance than other clustering methods.",
keywords = "2D embedding, Autoencoder, Community discovery, Semi-supervised learning, Single-cell clustering",
author = "Naile Shi and Yulin Wu and Linlin Du and Bo Liu and Yadong Wang and Junyi Li",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 17th International Conference on Intelligent Computing, ICIC 2021 ; Conference date: 12-08-2021 Through 15-08-2021",
year = "2021",
doi = "10.1007/978-3-030-84532-2\_43",
language = "英语",
isbn = "9783030845315",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "478--489",
editor = "De-Shuang Huang and Kang-Hyun Jo and Jianqiang Li and Valeriya Gribova and Vitoantonio Bevilacqua",
booktitle = "Intelligent Computing Theories and Application - 17th International Conference, ICIC 2021, Proceedings",
address = "德国",
}