Deep learning enables accurate alignment of single cell RNA-seq data

Yuanke Zhong; Jing Li; Jie Liu; Yan Zheng; Xuequn Shang; Jialu Hu

doi:10.1109/BIBM47256.2019.8982969

Deep learning enables accurate alignment of single cell RNA-seq data

Yuanke Zhong
, Jing Li
, Jie Liu
, Yan Zheng
, Xuequn Shang
, Jialu Hu

计算机学院

Northwestern Polytechnical University Xian

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

As more and more single-cell RNA-seq (scRNA-seq) datasets become available, carrying out compare between them is key. However, this task is challengeable due to differences caused by different experiment. We proposed a single cell alignment method using deep autoencoder followed by k-nearst-neighbor cells (scadKNN), which learns the feature representation of the data while eliminating batch effects and dropouts through deep autoencoder and uses the low-dimensional feature to align cell types, thereby reducing calculation effort and improving alignment accuracy. Experiments using different real datasets are employed to showcase the effectiveness of the proposed approach.

源语言	英语
主期刊名	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
编辑	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
出版商	Institute of Electrical and Electronics Engineers Inc.
页	778-781
页数	4
ISBN（电子版）	9781728118673
DOI	https://doi.org/10.1109/BIBM47256.2019.8982969
出版状态	已出版 - 11月 2019
活动	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, 美国期限: 18 11月 2019 → 21 11月 2019

出版系列

姓名	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

会议

会议	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
国家/地区	美国
市	San Diego
时期	18/11/19 → 21/11/19

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 3 良好健康与福祉

访问文件

10.1109/BIBM47256.2019.8982969

其它文件与链接

链接到 Scopus 的出版物

引用此

Zhong, Y., Li, J., Liu, J., Zheng, Y., Shang, X., & Hu, J. (2019). Deep learning enables accurate alignment of single cell RNA-seq data. 在 I. Yoo, J. Bi, & X. T. Hu (编辑), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (页码 778-781). 文章 8982969 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8982969

Zhong, Yuanke ; Li, Jing ; Liu, Jie 等. / Deep learning enables accurate alignment of single cell RNA-seq data. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. 编辑 / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. 页码 778-781 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

@inproceedings{368597560f3f40ec9193919692523fdd,

title = "Deep learning enables accurate alignment of single cell RNA-seq data",

abstract = "As more and more single-cell RNA-seq (scRNA-seq) datasets become available, carrying out compare between them is key. However, this task is challengeable due to differences caused by different experiment. We proposed a single cell alignment method using deep autoencoder followed by k-nearst-neighbor cells (scadKNN), which learns the feature representation of the data while eliminating batch effects and dropouts through deep autoencoder and uses the low-dimensional feature to align cell types, thereby reducing calculation effort and improving alignment accuracy. Experiments using different real datasets are employed to showcase the effectiveness of the proposed approach.",

keywords = "autoencoder, cell type alignment, Deep learning, feature representation, single cell RNA-seq",

author = "Yuanke Zhong and Jing Li and Jie Liu and Yan Zheng and Xuequn Shang and Jialu Hu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

year = "2019",

month = nov,

doi = "10.1109/BIBM47256.2019.8982969",

language = "英语",

series = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "778--781",

editor = "Illhoi Yoo and Jinbo Bi and Hu, \{Xiaohua Tony\}",

booktitle = "Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019",

}

Zhong, Y, Li, J, Liu, J, Zheng, Y, Shang, X & Hu, J 2019, Deep learning enables accurate alignment of single cell RNA-seq data. 在 I Yoo, J Bi & XT Hu (编辑), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8982969, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., 页码 778-781, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, 美国, 18/11/19. https://doi.org/10.1109/BIBM47256.2019.8982969

Deep learning enables accurate alignment of single cell RNA-seq data. / Zhong, Yuanke; Li, Jing; Liu, Jie 等.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. 编辑 / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. 页码 778-781 8982969 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Deep learning enables accurate alignment of single cell RNA-seq data

AU - Zhong, Yuanke

AU - Li, Jing

AU - Liu, Jie

AU - Zheng, Yan

AU - Shang, Xuequn

AU - Hu, Jialu

PY - 2019/11

Y1 - 2019/11

N2 - As more and more single-cell RNA-seq (scRNA-seq) datasets become available, carrying out compare between them is key. However, this task is challengeable due to differences caused by different experiment. We proposed a single cell alignment method using deep autoencoder followed by k-nearst-neighbor cells (scadKNN), which learns the feature representation of the data while eliminating batch effects and dropouts through deep autoencoder and uses the low-dimensional feature to align cell types, thereby reducing calculation effort and improving alignment accuracy. Experiments using different real datasets are employed to showcase the effectiveness of the proposed approach.

AB - As more and more single-cell RNA-seq (scRNA-seq) datasets become available, carrying out compare between them is key. However, this task is challengeable due to differences caused by different experiment. We proposed a single cell alignment method using deep autoencoder followed by k-nearst-neighbor cells (scadKNN), which learns the feature representation of the data while eliminating batch effects and dropouts through deep autoencoder and uses the low-dimensional feature to align cell types, thereby reducing calculation effort and improving alignment accuracy. Experiments using different real datasets are employed to showcase the effectiveness of the proposed approach.

KW - autoencoder

KW - cell type alignment

KW - Deep learning

KW - feature representation

KW - single cell RNA-seq

UR - https://www.scopus.com/pages/publications/85084334238

U2 - 10.1109/BIBM47256.2019.8982969

DO - 10.1109/BIBM47256.2019.8982969

M3 - 会议稿件

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T3 - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

SP - 778

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BT - Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

A2 - Yoo, Illhoi

A2 - Bi, Jinbo

A2 - Hu, Xiaohua Tony

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Y2 - 18 November 2019 through 21 November 2019

ER -

Zhong Y, Li J, Liu J, Zheng Y, Shang X, Hu J. Deep learning enables accurate alignment of single cell RNA-seq data. 在 Yoo I, Bi J, Hu XT, 编辑, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 页码 778-781. 8982969. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8982969

Deep learning enables accurate alignment of single cell RNA-seq data

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

学术指纹

引用此