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

Jingjie Xu, Razin Abdulrauf Shaikh, Vladimir Brusic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditorsHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1435-1438
Number of pages4
ISBN (Electronic)9781538654880
DOIs
Publication statusPublished - Jan 21 2019
Externally publishedYes
Event2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain
Duration: Dec 3 2018Dec 6 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
CountrySpain
CityMadrid
Period12/3/1812/6/18

Keywords

  • comparative analysis
  • PBMC
  • scRNAseq
  • single cell transcriptomics
  • transcriptomics signatures

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics

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