Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks

Razin Abdulrauf Shaikh; Jiahui Zhong; Minjie Lyu; Sen Lin; Derin Keskin; Guanglan Zhang; Lou Chitkushev; Vladimir Brusic

doi:10.1109/BIBM47256.2019.8983387

Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks

Razin Abdulrauf Shaikh, Jiahui Zhong, Minjie Lyu, Sen Lin, Derin Keskin, Guanglan Zhang, Lou Chitkushev, Vladimir Brusic

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

13 Citations (Scopus)

Abstract

We used 27 human single cell transcriptomics (SCT) data sets to develop an artificial neural network (ANN) model for classification of Peripheral Blood Mononuclear Cells (PBMC). We demonstrated that highly accurate models for the classification of PBMC subtypes can be developed by combining multiple independent data sets to form training data sets. A significant data preparation effort was needed for building predictive models. Using a data set of ∼120,000 single cell instances we showed the accuracy of classification of PBMC call of ∼ 90%. Optimization techniques and the addition of new high-quality data sets for model training are expected to improve PBMC subtype classification accuracy.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editors	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2207-2213
Number of pages	7
ISBN (Electronic)	9781728118673
DOIs	https://doi.org/10.1109/BIBM47256.2019.8983387
Publication status	Published - Nov 2019
Event	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: 18 Nov 2019 → 21 Nov 2019

Publication series

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

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Country/Territory	United States
City	San Diego
Period	18/11/19 → 21/11/19

Keywords

ANN
Machine Learning
PBMC
incremental learning
scRNAseq

ASJC Scopus subject areas

Biochemistry
Biotechnology
Molecular Medicine
Modelling and Simulation
Health Informatics
Pharmacology (medical)
Public Health, Environmental and Occupational Health

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10.1109/BIBM47256.2019.8983387

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Shaikh, R. A., Zhong, J., Lyu, M., Lin, S., Keskin, D., Zhang, G., Chitkushev, L., & Brusic, V. (2019). Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 2207-2213). Article 8983387 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983387

Shaikh, Razin Abdulrauf ; Zhong, Jiahui ; Lyu, Minjie et al. / Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2207-2213 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

@inproceedings{bbb177e104ca493dbeddd11ef730a024,

title = "Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks",

abstract = "We used 27 human single cell transcriptomics (SCT) data sets to develop an artificial neural network (ANN) model for classification of Peripheral Blood Mononuclear Cells (PBMC). We demonstrated that highly accurate models for the classification of PBMC subtypes can be developed by combining multiple independent data sets to form training data sets. A significant data preparation effort was needed for building predictive models. Using a data set of ∼120,000 single cell instances we showed the accuracy of classification of PBMC call of ∼ 90%. Optimization techniques and the addition of new high-quality data sets for model training are expected to improve PBMC subtype classification accuracy.",

keywords = "ANN, Machine Learning, PBMC, incremental learning, scRNAseq",

author = "Shaikh, {Razin Abdulrauf} and Jiahui Zhong and Minjie Lyu and Sen Lin and Derin Keskin and Guanglan Zhang and Lou Chitkushev and Vladimir Brusic",

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/BIBM47256.2019.8983387",

language = "English",

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

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

pages = "2207--2213",

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

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

address = "United States",

}

Shaikh, RA, Zhong, J, Lyu, M, Lin, S, Keskin, D, Zhang, G, Chitkushev, L & Brusic, V 2019, Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks. in I Yoo, J Bi & XT Hu (eds), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983387, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, Institute of Electrical and Electronics Engineers Inc., pp. 2207-2213, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 18/11/19. https://doi.org/10.1109/BIBM47256.2019.8983387

Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks. / Shaikh, Razin Abdulrauf; Zhong, Jiahui; Lyu, Minjie et al.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2207-2213 8983387 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

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

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T1 - Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks

AU - Shaikh, Razin Abdulrauf

AU - Zhong, Jiahui

AU - Lyu, Minjie

AU - Lin, Sen

AU - Keskin, Derin

AU - Zhang, Guanglan

AU - Chitkushev, Lou

AU - Brusic, Vladimir

PY - 2019/11

Y1 - 2019/11

N2 - We used 27 human single cell transcriptomics (SCT) data sets to develop an artificial neural network (ANN) model for classification of Peripheral Blood Mononuclear Cells (PBMC). We demonstrated that highly accurate models for the classification of PBMC subtypes can be developed by combining multiple independent data sets to form training data sets. A significant data preparation effort was needed for building predictive models. Using a data set of ∼120,000 single cell instances we showed the accuracy of classification of PBMC call of ∼ 90%. Optimization techniques and the addition of new high-quality data sets for model training are expected to improve PBMC subtype classification accuracy.

AB - We used 27 human single cell transcriptomics (SCT) data sets to develop an artificial neural network (ANN) model for classification of Peripheral Blood Mononuclear Cells (PBMC). We demonstrated that highly accurate models for the classification of PBMC subtypes can be developed by combining multiple independent data sets to form training data sets. A significant data preparation effort was needed for building predictive models. Using a data set of ∼120,000 single cell instances we showed the accuracy of classification of PBMC call of ∼ 90%. Optimization techniques and the addition of new high-quality data sets for model training are expected to improve PBMC subtype classification accuracy.

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KW - Machine Learning

KW - PBMC

KW - incremental learning

KW - scRNAseq

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M3 - Conference contribution

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

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Shaikh RA, Zhong J, Lyu M, Lin S, Keskin D, Zhang G et al. Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks. In Yoo I, Bi J, Hu XT, editors, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2207-2213. 8983387. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983387

Classification of Five Cell Types from PBMC Samples using Single Cell Transcriptomics and Artificial Neural Networks

Abstract

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Keywords

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