Automation of Gene Expression Profile Analysis in Single Cell Data

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

We designed and implemented an automated system, named Pierse for pattern recognition of single cell transcriptomics (SCT) data. The Pierse system takes sparse matrices and corresponding metadata as input to generate SCDC profiles (SCT gene expression profiles characteristic of types or subtypes of cells). These profiles can be used for profile comparison, feature extraction, and differential gene expression analysis. Hierarchical clustering is used for similarity analysis between SCDC profiles and resulting heatmaps are produced. We performed a demonstration study to test functional modules in the Pierse system. To improve efficiency, we deployed parallel programming scripts and implemented efficient matrix analysis functions in the demonstration study.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
EditorsTaesung Park, Young-Rae Cho, Xiaohua Tony Hu, Illhoi Yoo, Hyun Goo Woo, Jianxin Wang, Julio Facelli, Seungyoon Nam, Mingon Kang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1329-1334
Number of pages6
ISBN (Electronic)9781728162157
DOIs
Publication statusPublished - 16 Dec 2020
Event2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020 - Virtual, Seoul, Korea, Republic of
Duration: 16 Dec 202019 Dec 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

Conference

Conference2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
Country/TerritoryKorea, Republic of
CityVirtual, Seoul
Period16/12/2019/12/20

Keywords

  • SCT pattern recognition system
  • gene expression profiles
  • hierarchical clustering
  • profile comparison

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management
  • Medicine (miscellaneous)
  • Health Informatics

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