Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types

Jingjie Xu; Razin Abdulrauf Shaikh; Vladimir Brusic

doi:10.1109/BIBM.2018.8621396

Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types

Jingjie Xu, Razin Abdulrauf Shaikh, Vladimir Brusic

School of Economics

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

2 Citations (Scopus)

Abstract

Single cell transcriptomics (SCT) reveals cellular patterns that are masked and hidden in bulk RNA experiments. We analyzed 100 human SCT data sets for summary patterns that quantify gene expression per individual cell as well as per gene. Peripheral Blood Mononuclear Cells (PBMCs) show patterns different to those of cancer cell lines, stem cells, embryonic stem cells and other cell types. The results indicate that classification methods based on overall properties of SCT data sets provide a useful first step for classification of cell types and subtypes.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1435-1438
Number of pages	4
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621396
Publication status	Published - 21 Jan 2019
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: 3 Dec 2018 → 6 Dec 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	3/12/18 → 6/12/18

Keywords

PBMC
comparative analysis
scRNAseq
single cell transcriptomics
transcriptomics signatures

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/BIBM.2018.8621396

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Xu, J., Shaikh, R. A., & Brusic, V. (2019). Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 1435-1438). Article 8621396 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621396

Xu, Jingjie ; Shaikh, Razin Abdulrauf ; Brusic, Vladimir. / Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1435-1438 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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title = "Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types",

abstract = "Single cell transcriptomics (SCT) reveals cellular patterns that are masked and hidden in bulk RNA experiments. We analyzed 100 human SCT data sets for summary patterns that quantify gene expression per individual cell as well as per gene. Peripheral Blood Mononuclear Cells (PBMCs) show patterns different to those of cancer cell lines, stem cells, embryonic stem cells and other cell types. The results indicate that classification methods based on overall properties of SCT data sets provide a useful first step for classification of cell types and subtypes.",

keywords = "PBMC, comparative analysis, scRNAseq, single cell transcriptomics, transcriptomics signatures",

author = "Jingjie Xu and Shaikh, {Razin Abdulrauf} and Vladimir Brusic",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

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doi = "10.1109/BIBM.2018.8621396",

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editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",

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Xu, J, Shaikh, RA & Brusic, V 2019, Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621396, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1435-1438, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 3/12/18. https://doi.org/10.1109/BIBM.2018.8621396

Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types. / Xu, Jingjie; Shaikh, Razin Abdulrauf; Brusic, Vladimir.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1435-1438 8621396 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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

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AU - Shaikh, Razin Abdulrauf

AU - Brusic, Vladimir

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N2 - Single cell transcriptomics (SCT) reveals cellular patterns that are masked and hidden in bulk RNA experiments. We analyzed 100 human SCT data sets for summary patterns that quantify gene expression per individual cell as well as per gene. Peripheral Blood Mononuclear Cells (PBMCs) show patterns different to those of cancer cell lines, stem cells, embryonic stem cells and other cell types. The results indicate that classification methods based on overall properties of SCT data sets provide a useful first step for classification of cell types and subtypes.

AB - Single cell transcriptomics (SCT) reveals cellular patterns that are masked and hidden in bulk RNA experiments. We analyzed 100 human SCT data sets for summary patterns that quantify gene expression per individual cell as well as per gene. Peripheral Blood Mononuclear Cells (PBMCs) show patterns different to those of cancer cell lines, stem cells, embryonic stem cells and other cell types. The results indicate that classification methods based on overall properties of SCT data sets provide a useful first step for classification of cell types and subtypes.

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BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

A2 - Schmidt, Harald

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A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

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Xu J, Shaikh RA, Brusic V. Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1435-1438. 8621396. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621396

Single Cell Transcriptomics Reveals Summary Patterns Specific for PBMCs and Other Cell Types

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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