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 proceedingConference contributionpeer-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 languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
EditorsTaesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1346-1350
Number of pages5
ISBN (Electronic)9781728162157
DOIs
Publication statusPublished - 16 Dec 2020
Event2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of
Duration: 16 Dec 202019 Dec 2020

Publication series

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

Conference

Conference2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/TerritoryKorea, Republic of
CityVirtual, Seoul
Period16/12/2019/12/20

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

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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