Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks

Bangrui Zheng; Minjie Lyu; Sen Lin; Vladimir Brusic

doi:10.1109/BIBM49941.2020.9313427

Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks

Bangrui Zheng, Minjie Lyu, Sen Lin, Vladimir Brusic

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

1 Citation (Scopus)

Abstract

Single cell transcriptomics (SCT) enables high-throughput measurement of mRNA expression concurrently from tens of thousands of single cells. Gene expression profiles in single cells cover only a small fraction of expressed genes and these data are inherently noisy. We developed a method that utilizes artificial neural networks (ANN) for classification of single cells by their tissue of origin. Data sets representing 10 different organs and tissues from C57BL/6 laboratory mice were standardized and used for training and testing ANN models. Each organ was represented by at least two datasets derived from different mice. We achieved 80% accuracy in 10-class classification. After combining data sets from spleen, bone marrow, and lung into one super-class and mammary tissue and muscle into another, we achieved overall cell classification accuracy of 98% across two tissue super-classes and five organs.

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	1346-1350
Number of pages	5
ISBN (Electronic)	9781728162157
DOIs	https://doi.org/10.1109/BIBM49941.2020.9313427
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

classification of single cells
hierarchical classification
single cell gene expression
super-class
supervised machine learning

ASJC Scopus subject areas

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

Access to Document

10.1109/BIBM49941.2020.9313427

Cite this

Zheng, B., Lyu, M., Lin, S., & Brusic, V. (2020). Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks. 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. 1346-1350). Article 9313427 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM49941.2020.9313427

Zheng, Bangrui ; Lyu, Minjie ; Lin, Sen et al. / Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks. 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. 1346-1350 (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020).

@inproceedings{c253db5b3f324cacbdca79856ba7c336,

title = "Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks",

abstract = "Single cell transcriptomics (SCT) enables high-throughput measurement of mRNA expression concurrently from tens of thousands of single cells. Gene expression profiles in single cells cover only a small fraction of expressed genes and these data are inherently noisy. We developed a method that utilizes artificial neural networks (ANN) for classification of single cells by their tissue of origin. Data sets representing 10 different organs and tissues from C57BL/6 laboratory mice were standardized and used for training and testing ANN models. Each organ was represented by at least two datasets derived from different mice. We achieved 80% accuracy in 10-class classification. After combining data sets from spleen, bone marrow, and lung into one super-class and mammary tissue and muscle into another, we achieved overall cell classification accuracy of 98% across two tissue super-classes and five organs.",

keywords = "classification of single cells, hierarchical classification, single cell gene expression, super-class, supervised machine learning",

author = "Bangrui Zheng and Minjie Lyu and Sen Lin 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/BIBM49941.2020.9313427",

language = "English",

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

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

pages = "1346--1350",

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",

}

Zheng, B, Lyu, M, Lin, S & Brusic, V 2020, Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks. 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., 9313427, Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 1346-1350, 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020, Virtual, Seoul, Korea, Republic of, 16/12/20. https://doi.org/10.1109/BIBM49941.2020.9313427

Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks. / Zheng, Bangrui; Lyu, Minjie; Lin, Sen 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. 1346-1350 9313427 (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 - Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks

AU - Zheng, Bangrui

AU - Lyu, Minjie

AU - Lin, Sen

AU - Brusic, Vladimir

PY - 2020/12/16

Y1 - 2020/12/16

N2 - Single cell transcriptomics (SCT) enables high-throughput measurement of mRNA expression concurrently from tens of thousands of single cells. Gene expression profiles in single cells cover only a small fraction of expressed genes and these data are inherently noisy. We developed a method that utilizes artificial neural networks (ANN) for classification of single cells by their tissue of origin. Data sets representing 10 different organs and tissues from C57BL/6 laboratory mice were standardized and used for training and testing ANN models. Each organ was represented by at least two datasets derived from different mice. We achieved 80% accuracy in 10-class classification. After combining data sets from spleen, bone marrow, and lung into one super-class and mammary tissue and muscle into another, we achieved overall cell classification accuracy of 98% across two tissue super-classes and five organs.

AB - Single cell transcriptomics (SCT) enables high-throughput measurement of mRNA expression concurrently from tens of thousands of single cells. Gene expression profiles in single cells cover only a small fraction of expressed genes and these data are inherently noisy. We developed a method that utilizes artificial neural networks (ANN) for classification of single cells by their tissue of origin. Data sets representing 10 different organs and tissues from C57BL/6 laboratory mice were standardized and used for training and testing ANN models. Each organ was represented by at least two datasets derived from different mice. We achieved 80% accuracy in 10-class classification. After combining data sets from spleen, bone marrow, and lung into one super-class and mammary tissue and muscle into another, we achieved overall cell classification accuracy of 98% across two tissue super-classes and five organs.

KW - classification of single cells

KW - hierarchical classification

KW - single cell gene expression

KW - super-class

KW - supervised machine learning

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U2 - 10.1109/BIBM49941.2020.9313427

DO - 10.1109/BIBM49941.2020.9313427

M3 - Conference contribution

AN - SCOPUS:85100338460

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

SP - 1346

EP - 1350

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 -

Zheng B, Lyu M, Lin S, Brusic V. Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks. 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. 1346-1350. 9313427. (Proceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020). doi: 10.1109/BIBM49941.2020.9313427

Tissue of origin classification from single cell mRNA expression by Artificial Neural Networks

Abstract

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Keywords

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