Classification of Single Cell Types using Small Sets of Expressed Genes: Comparative Analysis of Supervised Machine Learning Methods

Aleksandar Veljkovic; Mirjana Maljkovic; Nenad Mitic; Sasa Malkov; Minjie Lyu; Xin Lin; Marek Michalewicz; Guanglan Zhang; Vladimir Brusic

doi:10.1109/BIBM52615.2021.9669844

Classification of Single Cell Types using Small Sets of Expressed Genes: Comparative Analysis of Supervised Machine Learning Methods

Aleksandar Veljkovic
, Mirjana Maljkovic
, Nenad Mitic
, Sasa Malkov
, Minjie Lyu
, Xin Lin
, Marek Michalewicz
, Guanglan Zhang
, Vladimir Brusic

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

1 Citation (Scopus)

Abstract

Single cell transcriptomics measures gene expression data of large number of genes, concurrently, from tens of thousands of cells present in a studied biological sample. It is difficult to obtain good classification results due to high data dimensionality and variability of biological states. We performed a preliminary study to assess the feasibility of using supervised machine learning methods to classify peripheral blood mononuclear cell (PBMC) types from single cell gene expression data. We analyzed a large PBMC data set (sim 120,000 PBMC cells), selected 47 genes (from 30698 features) suitable as SML classification features, and performed classification using 20 machine learning algorithms. Data sets represented three sample processing strategies: PBMC separation (two data sets), and experimental cell sorting by (two data sets). The accuracy in 5-class classification among 20 methods was 91-97% (PBMC separation), 97-100% (magnetic-activated cell sorting), and 82-99% (fluorescence-activated cell sorting). Our results indicate the feasibility of supervised machine learning for classification of cells into major PBMC cell types using a small number of classification features from single cell gene expression data.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Editors	Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3322-3326
Number of pages	5
ISBN (Electronic)	9781665401265
DOIs	https://doi.org/10.1109/BIBM52615.2021.9669844
Publication status	Published - 2021
Event	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States Duration: 9 Dec 2021 → 12 Dec 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/Territory	United States
City	Virtual, Online
Period	9/12/21 → 12/12/21

Free Keywords

10x SCT
PBMC
classification
data mining
dimensionality reduction
gene expression
machine learning
transcriptome

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Biomedical Engineering
Health Informatics
Information Systems and Management

Access to Document

10.1109/BIBM52615.2021.9669844

Cite this

Veljkovic, A., Maljkovic, M., Mitic, N., Malkov, S., Lyu, M., Lin, X., Michalewicz, M., Zhang, G., & Brusic, V. (2021). Classification of Single Cell Types using Small Sets of Expressed Genes: Comparative Analysis of Supervised Machine Learning Methods. In Y. Huang, L. Kurgan, F. Luo, X. T. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (pp. 3322-3326). (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM52615.2021.9669844

Veljkovic, Aleksandar ; Maljkovic, Mirjana ; Mitic, Nenad et al. / Classification of Single Cell Types using Small Sets of Expressed Genes : Comparative Analysis of Supervised Machine Learning Methods. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. editor / Yufei Huang ; Lukasz Kurgan ; Feng Luo ; Xiaohua Tony Hu ; Yidong Chen ; Edward Dougherty ; Andrzej Kloczkowski ; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 3322-3326 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

@inproceedings{f9c4cdbc2ad344d2a57ec374132aa430,

title = "Classification of Single Cell Types using Small Sets of Expressed Genes: Comparative Analysis of Supervised Machine Learning Methods",

abstract = "Single cell transcriptomics measures gene expression data of large number of genes, concurrently, from tens of thousands of cells present in a studied biological sample. It is difficult to obtain good classification results due to high data dimensionality and variability of biological states. We performed a preliminary study to assess the feasibility of using supervised machine learning methods to classify peripheral blood mononuclear cell (PBMC) types from single cell gene expression data. We analyzed a large PBMC data set (sim 120,000 PBMC cells), selected 47 genes (from 30698 features) suitable as SML classification features, and performed classification using 20 machine learning algorithms. Data sets represented three sample processing strategies: PBMC separation (two data sets), and experimental cell sorting by (two data sets). The accuracy in 5-class classification among 20 methods was 91-97\% (PBMC separation), 97-100\% (magnetic-activated cell sorting), and 82-99\% (fluorescence-activated cell sorting). Our results indicate the feasibility of supervised machine learning for classification of cells into major PBMC cell types using a small number of classification features from single cell gene expression data.",

keywords = "10x SCT, PBMC, classification, data mining, dimensionality reduction, gene expression, machine learning, transcriptome",

author = "Aleksandar Veljkovic and Mirjana Maljkovic and Nenad Mitic and Sasa Malkov and Minjie Lyu and Xin Lin and Marek Michalewicz and Guanglan Zhang and Vladimir Brusic",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1109/BIBM52615.2021.9669844",

language = "English",

series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

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

pages = "3322--3326",

editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, \{Xiaohua Tony\} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",

booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

address = "United States",

}

Veljkovic, A, Maljkovic, M, Mitic, N, Malkov, S, Lyu, M, Lin, X, Michalewicz, M, Zhang, G & Brusic, V 2021, Classification of Single Cell Types using Small Sets of Expressed Genes: Comparative Analysis of Supervised Machine Learning Methods. in Y Huang, L Kurgan, F Luo, XT Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Institute of Electrical and Electronics Engineers Inc., pp. 3322-3326, 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, United States, 9/12/21. https://doi.org/10.1109/BIBM52615.2021.9669844

Classification of Single Cell Types using Small Sets of Expressed Genes: Comparative Analysis of Supervised Machine Learning Methods. / Veljkovic, Aleksandar; Maljkovic, Mirjana; Mitic, Nenad et al.
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. ed. / Yufei Huang; Lukasz Kurgan; Feng Luo; Xiaohua Tony Hu; Yidong Chen; Edward Dougherty; Andrzej Kloczkowski; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. p. 3322-3326 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

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

TY - GEN

T1 - Classification of Single Cell Types using Small Sets of Expressed Genes

T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

AU - Veljkovic, Aleksandar

AU - Maljkovic, Mirjana

AU - Mitic, Nenad

AU - Malkov, Sasa

AU - Lyu, Minjie

AU - Lin, Xin

AU - Michalewicz, Marek

AU - Zhang, Guanglan

AU - Brusic, Vladimir

PY - 2021

Y1 - 2021

N2 - Single cell transcriptomics measures gene expression data of large number of genes, concurrently, from tens of thousands of cells present in a studied biological sample. It is difficult to obtain good classification results due to high data dimensionality and variability of biological states. We performed a preliminary study to assess the feasibility of using supervised machine learning methods to classify peripheral blood mononuclear cell (PBMC) types from single cell gene expression data. We analyzed a large PBMC data set (sim 120,000 PBMC cells), selected 47 genes (from 30698 features) suitable as SML classification features, and performed classification using 20 machine learning algorithms. Data sets represented three sample processing strategies: PBMC separation (two data sets), and experimental cell sorting by (two data sets). The accuracy in 5-class classification among 20 methods was 91-97% (PBMC separation), 97-100% (magnetic-activated cell sorting), and 82-99% (fluorescence-activated cell sorting). Our results indicate the feasibility of supervised machine learning for classification of cells into major PBMC cell types using a small number of classification features from single cell gene expression data.

AB - Single cell transcriptomics measures gene expression data of large number of genes, concurrently, from tens of thousands of cells present in a studied biological sample. It is difficult to obtain good classification results due to high data dimensionality and variability of biological states. We performed a preliminary study to assess the feasibility of using supervised machine learning methods to classify peripheral blood mononuclear cell (PBMC) types from single cell gene expression data. We analyzed a large PBMC data set (sim 120,000 PBMC cells), selected 47 genes (from 30698 features) suitable as SML classification features, and performed classification using 20 machine learning algorithms. Data sets represented three sample processing strategies: PBMC separation (two data sets), and experimental cell sorting by (two data sets). The accuracy in 5-class classification among 20 methods was 91-97% (PBMC separation), 97-100% (magnetic-activated cell sorting), and 82-99% (fluorescence-activated cell sorting). Our results indicate the feasibility of supervised machine learning for classification of cells into major PBMC cell types using a small number of classification features from single cell gene expression data.

KW - 10x SCT

KW - PBMC

KW - classification

KW - data mining

KW - dimensionality reduction

KW - gene expression

KW - machine learning

KW - transcriptome

UR - https://www.scopus.com/pages/publications/85125205373

U2 - 10.1109/BIBM52615.2021.9669844

DO - 10.1109/BIBM52615.2021.9669844

M3 - Conference contribution

AN - SCOPUS:85125205373

T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

SP - 3322

EP - 3326

BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

A2 - Huang, Yufei

A2 - Kurgan, Lukasz

A2 - Luo, Feng

A2 - Hu, Xiaohua Tony

A2 - Chen, Yidong

A2 - Dougherty, Edward

A2 - Kloczkowski, Andrzej

A2 - Li, Yaohang

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 9 December 2021 through 12 December 2021

ER -

Veljkovic A, Maljkovic M, Mitic N, Malkov S, Lyu M, Lin X et al. Classification of Single Cell Types using Small Sets of Expressed Genes: Comparative Analysis of Supervised Machine Learning Methods. In Huang Y, Kurgan L, Luo F, Hu XT, Chen Y, Dougherty E, Kloczkowski A, Li Y, editors, Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 3322-3326. (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). doi: 10.1109/BIBM52615.2021.9669844

Classification of Single Cell Types using Small Sets of Expressed Genes: Comparative Analysis of Supervised Machine Learning Methods

Abstract

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Advanced supervised machine learning methods for single cell transcriptome analysis

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Classification of Single Cell Types using Small Sets of Expressed Genes: Comparative Analysis of Supervised Machine Learning Methods

Abstract

Publication series

Conference

Free Keywords

ASJC Scopus subject areas

Access to Document

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Fingerprint

Student theses

Advanced supervised machine learning methods for single cell transcriptome analysis

Cite this