Correctness of Cell Labels in Public Single Cell Transcriptomics Datasets

Xin Lin; Minjie Lyu; Yihan Zhang; Derin B. Keskin; Lou T. Chitkushev; Guanglan Zhang; Vladimir Brusic

doi:10.1109/BIBM52615.2021.9669297

Correctness of Cell Labels in Public Single Cell Transcriptomics Datasets

Xin Lin, Minjie Lyu, Yihan Zhang, Derin B. Keskin, Lou T. Chitkushev, Guanglan Zhang, Vladimir Brusic

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

Abstract

The number of single-cell transcriptomic (SCT) studies is rapidly increasing. More than 15000 single cell gene expression data sets are available in public repositories. More than 2400 of these sets involve Peripheral Blood Mononuclear Cells (PBMC) data sets. Main cell types of PBMC are B cells, dendritic cells, monocytes, natural killer cells, and T cells. Labels of individual PBMC are usually provided in metadata accompanying the data sets or are implicit as data set partitions for sorted cells. We analyzed the correctness of labels assigned to individual cells from PBMC in primary reports. The correctness of primary labels was assessed by using Artificial Neural Network (ANN) classifier and Confident Learning (CL) approach. We assessed that the number of mislabels on average in our data sets is about2%. The label accuracy varied broadly between data sets, particularly among those generated by experimental cell sorting followed by SCT.

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	3280-3284
Number of pages	5
ISBN (Electronic)	9781665401265
DOIs	https://doi.org/10.1109/BIBM52615.2021.9669297
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

Keywords

ANN
PBMC
confident learning
gene expression
mislabels analysis
supervised machine learning

ASJC Scopus subject areas

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

Access to Document

10.1109/BIBM52615.2021.9669297

Cite this

Lin, X., Lyu, M., Zhang, Y., Keskin, D. B., Chitkushev, L. T., Zhang, G., & Brusic, V. (2021). Correctness of Cell Labels in Public Single Cell Transcriptomics Datasets. 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. 3280-3284). (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM52615.2021.9669297

Lin, Xin ; Lyu, Minjie ; Zhang, Yihan et al. / Correctness of Cell Labels in Public Single Cell Transcriptomics Datasets. 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. 3280-3284 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

@inproceedings{7cea68b946cf458aa1072523e649c47d,

title = "Correctness of Cell Labels in Public Single Cell Transcriptomics Datasets",

abstract = "The number of single-cell transcriptomic (SCT) studies is rapidly increasing. More than 15000 single cell gene expression data sets are available in public repositories. More than 2400 of these sets involve Peripheral Blood Mononuclear Cells (PBMC) data sets. Main cell types of PBMC are B cells, dendritic cells, monocytes, natural killer cells, and T cells. Labels of individual PBMC are usually provided in metadata accompanying the data sets or are implicit as data set partitions for sorted cells. We analyzed the correctness of labels assigned to individual cells from PBMC in primary reports. The correctness of primary labels was assessed by using Artificial Neural Network (ANN) classifier and Confident Learning (CL) approach. We assessed that the number of mislabels on average in our data sets is about2%. The label accuracy varied broadly between data sets, particularly among those generated by experimental cell sorting followed by SCT.",

keywords = "ANN, PBMC, confident learning, gene expression, mislabels analysis, supervised machine learning",

author = "Xin Lin and Minjie Lyu and Yihan Zhang and Keskin, {Derin B.} and Chitkushev, {Lou T.} 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/BIBM52615.2021.9669297",

language = "English",

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

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

pages = "3280--3284",

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",

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address = "United States",

}

Lin, X , Lyu, M , Zhang, Y, Keskin, DB, Chitkushev, LT, Zhang, G & Brusic, V 2021, Correctness of Cell Labels in Public Single Cell Transcriptomics Datasets. 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. 3280-3284, 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, United States, 9/12/21. https://doi.org/10.1109/BIBM52615.2021.9669297

Correctness of Cell Labels in Public Single Cell Transcriptomics Datasets. / Lin, Xin ; Lyu, Minjie ; Zhang, Yihan 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. 3280-3284 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

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

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AU - Lin, Xin

AU - Lyu, Minjie

AU - Zhang, Yihan

AU - Keskin, Derin B.

AU - Chitkushev, Lou T.

AU - Zhang, Guanglan

AU - Brusic, Vladimir

PY - 2021

Y1 - 2021

N2 - The number of single-cell transcriptomic (SCT) studies is rapidly increasing. More than 15000 single cell gene expression data sets are available in public repositories. More than 2400 of these sets involve Peripheral Blood Mononuclear Cells (PBMC) data sets. Main cell types of PBMC are B cells, dendritic cells, monocytes, natural killer cells, and T cells. Labels of individual PBMC are usually provided in metadata accompanying the data sets or are implicit as data set partitions for sorted cells. We analyzed the correctness of labels assigned to individual cells from PBMC in primary reports. The correctness of primary labels was assessed by using Artificial Neural Network (ANN) classifier and Confident Learning (CL) approach. We assessed that the number of mislabels on average in our data sets is about2%. The label accuracy varied broadly between data sets, particularly among those generated by experimental cell sorting followed by SCT.

AB - The number of single-cell transcriptomic (SCT) studies is rapidly increasing. More than 15000 single cell gene expression data sets are available in public repositories. More than 2400 of these sets involve Peripheral Blood Mononuclear Cells (PBMC) data sets. Main cell types of PBMC are B cells, dendritic cells, monocytes, natural killer cells, and T cells. Labels of individual PBMC are usually provided in metadata accompanying the data sets or are implicit as data set partitions for sorted cells. We analyzed the correctness of labels assigned to individual cells from PBMC in primary reports. The correctness of primary labels was assessed by using Artificial Neural Network (ANN) classifier and Confident Learning (CL) approach. We assessed that the number of mislabels on average in our data sets is about2%. The label accuracy varied broadly between data sets, particularly among those generated by experimental cell sorting followed by SCT.

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M3 - Conference contribution

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SP - 3280

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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

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Lin X , Lyu M , Zhang Y, Keskin DB, Chitkushev LT, Zhang G et al. Correctness of Cell Labels in Public Single Cell Transcriptomics Datasets. 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. 3280-3284. (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). doi: 10.1109/BIBM52615.2021.9669297

Correctness of Cell Labels in Public Single Cell Transcriptomics Datasets

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Keywords

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