Classification of PBMC cell types using scRNAseq, ANN, and incremental learning

Jiahui Zhong; Razin A. Shaikh; Haoguo Wu; Xin Lin; Zhiwei Cao; Lou T. Chitkushev; Guanglan Zhang; Derin B. Keskin; Vladimir Brusic

doi:10.1109/BIBM49941.2020.9313519

Classification of PBMC cell types using scRNAseq, ANN, and incremental learning

Jiahui Zhong, Razin A. Shaikh, Haoguo Wu, Xin Lin, Zhiwei Cao, Lou T. Chitkushev, Guanglan Zhang, Derin B. Keskin, Vladimir Brusic

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Single cell transcriptomics (SCT) technology reveals gene expression of individual cells. Peripheral blood mononuclear cells (PBMC) are important diagnostic targets in immunology. In this study, we obtained and standardized 27 SCT data sets, derived from healthy PBMC samples using 10x SCT. We used artificial neural networks (ANN) to assess the ability of ANN to classify main PBMC cell types. Incremental learning by the gradual addition of new data sets to ANN training improved classification. The overall prediction accuracy of the final step of incremental learning reached 93% in 4-class classification.

Original language	English
Title of host publication	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Editors	Taesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1351-1355
Number of pages	5
ISBN (Electronic)	9781728162157
DOIs	https://doi.org/10.1109/BIBM49941.2020.9313519
Publication status	Published - 16 Dec 2020
Event	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of Duration: 16 Dec 2020 → 19 Dec 2020

Publication series

Name	Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference	2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/Territory	Korea, Republic of
City	Virtual, Seoul
Period	16/12/20 → 19/12/20

Keywords

10x SCT
Artificial Neural Networks
PBMC
incremental learning
supervised machine learning

ASJC Scopus subject areas

Computer Science Applications
Information Systems and Management
Medicine (miscellaneous)
Health Informatics

Access to Document

10.1109/BIBM49941.2020.9313519

Cite this

Zhong, J., Shaikh, R. A., Wu, H., Lin, X., Cao, Z., Chitkushev, L. T., Zhang, G., Keskin, D. B., & Brusic, V. (2020). Classification of PBMC cell types using scRNAseq, ANN, and incremental learning. In T. Park, Y.-R. Cho, X. T. Hu, I. Yoo, H. G. Woo, J. Wang, J. Facelli, S. Nam, & M. Kang (Eds.), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 (pp. 1351-1355). Article 9313519 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM49941.2020.9313519

Zhong, Jiahui ; Shaikh, Razin A. ; Wu, Haoguo et al. / Classification of PBMC cell types using scRNAseq, ANN, and incremental learning. Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. editor / Taesung Park ; Young-Rae Cho ; Xiaohua Tony Hu ; Illhoi Yoo ; Hyun Goo Woo ; Jianxin Wang ; Julio Facelli ; Seungyoon Nam ; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1351-1355 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

@inproceedings{79182f3898a64d96b7a73fc84702d71f,

title = "Classification of PBMC cell types using scRNAseq, ANN, and incremental learning",

abstract = "Single cell transcriptomics (SCT) technology reveals gene expression of individual cells. Peripheral blood mononuclear cells (PBMC) are important diagnostic targets in immunology. In this study, we obtained and standardized 27 SCT data sets, derived from healthy PBMC samples using 10x SCT. We used artificial neural networks (ANN) to assess the ability of ANN to classify main PBMC cell types. Incremental learning by the gradual addition of new data sets to ANN training improved classification. The overall prediction accuracy of the final step of incremental learning reached 93% in 4-class classification.",

keywords = "10x SCT, Artificial Neural Networks, PBMC, incremental learning, supervised machine learning",

author = "Jiahui Zhong and Shaikh, {Razin A.} and Haoguo Wu and Xin Lin and Zhiwei Cao and Chitkushev, {Lou T.} and Guanglan Zhang and Keskin, {Derin B.} and Vladimir Brusic",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 ; Conference date: 16-12-2020 Through 19-12-2020",

year = "2020",

month = dec,

day = "16",

doi = "10.1109/BIBM49941.2020.9313519",

language = "English",

series = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

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

pages = "1351--1355",

editor = "Taesung Park and Young-Rae Cho and Hu, {Xiaohua Tony} and Illhoi Yoo and Woo, {Hyun Goo} and Jianxin Wang and Julio Facelli and Seungyoon Nam and Mingon Kang",

booktitle = "Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020",

address = "United States",

}

Zhong, J, Shaikh, RA, Wu, H, Lin, X, Cao, Z, Chitkushev, LT, Zhang, G, Keskin, DB & Brusic, V 2020, Classification of PBMC cell types using scRNAseq, ANN, and incremental learning. in T Park, Y-R Cho, XT Hu, I Yoo, HG Woo, J Wang, J Facelli, S Nam & M Kang (eds), Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020., 9313519, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1351-1355, 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Virtual, Seoul, Korea, Republic of, 16/12/20. https://doi.org/10.1109/BIBM49941.2020.9313519

Classification of PBMC cell types using scRNAseq, ANN, and incremental learning. / Zhong, Jiahui; Shaikh, Razin A.; Wu, Haoguo et al.
Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. ed. / Taesung Park; Young-Rae Cho; Xiaohua Tony Hu; Illhoi Yoo; Hyun Goo Woo; Jianxin Wang; Julio Facelli; Seungyoon Nam; Mingon Kang. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1351-1355 9313519 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classification of PBMC cell types using scRNAseq, ANN, and incremental learning

AU - Zhong, Jiahui

AU - Shaikh, Razin A.

AU - Wu, Haoguo

AU - Lin, Xin

AU - Cao, Zhiwei

AU - Chitkushev, Lou T.

AU - Zhang, Guanglan

AU - Keskin, Derin B.

AU - Brusic, Vladimir

PY - 2020/12/16

Y1 - 2020/12/16

N2 - Single cell transcriptomics (SCT) technology reveals gene expression of individual cells. Peripheral blood mononuclear cells (PBMC) are important diagnostic targets in immunology. In this study, we obtained and standardized 27 SCT data sets, derived from healthy PBMC samples using 10x SCT. We used artificial neural networks (ANN) to assess the ability of ANN to classify main PBMC cell types. Incremental learning by the gradual addition of new data sets to ANN training improved classification. The overall prediction accuracy of the final step of incremental learning reached 93% in 4-class classification.

AB - Single cell transcriptomics (SCT) technology reveals gene expression of individual cells. Peripheral blood mononuclear cells (PBMC) are important diagnostic targets in immunology. In this study, we obtained and standardized 27 SCT data sets, derived from healthy PBMC samples using 10x SCT. We used artificial neural networks (ANN) to assess the ability of ANN to classify main PBMC cell types. Incremental learning by the gradual addition of new data sets to ANN training improved classification. The overall prediction accuracy of the final step of incremental learning reached 93% in 4-class classification.

KW - 10x SCT

KW - Artificial Neural Networks

KW - PBMC

KW - incremental learning

KW - supervised machine learning

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DO - 10.1109/BIBM49941.2020.9313519

M3 - Conference contribution

AN - SCOPUS:85100344468

T3 - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

SP - 1351

EP - 1355

BT - Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

A2 - Park, Taesung

A2 - Cho, Young-Rae

A2 - Hu, Xiaohua Tony

A2 - Yoo, Illhoi

A2 - Woo, Hyun Goo

A2 - Wang, Jianxin

A2 - Facelli, Julio

A2 - Nam, Seungyoon

A2 - Kang, Mingon

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Y2 - 16 December 2020 through 19 December 2020

ER -

Zhong J, Shaikh RA, Wu H, Lin X, Cao Z, Chitkushev LT et al. Classification of PBMC cell types using scRNAseq, ANN, and incremental learning. In Park T, Cho YR, Hu XT, Yoo I, Woo HG, Wang J, Facelli J, Nam S, Kang M, editors, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1351-1355. 9313519. (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). doi: 10.1109/BIBM49941.2020.9313519

Classification of PBMC cell types using scRNAseq, ANN, and incremental learning

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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