Abstract
The advent of droplet-based transcriptomics platforms has enabled parallel screening over thousands or millions of cells. One of the challenging issues is to identify the rare cells from the ultra-large scRNA-seq data. Existing algorithms to find rare cells are time consuming or memory-exhausting. We propose an efficient and accurate method, Discovery of Rare Cells (DoRC). The rareness scores generated by DoRC can help biologists focus the downstream analyses only on a fraction of expression profiles within ultra-large scRNA-seq data. We also demonstrate the efficacy of DoRC in delineating human blood dendritic cell sub-types using ∼68k single-cell expression profiles of human blood cells. DoRC can recover artificially planted rare cells and is sensitive to cell type identities as well.
Original language | English |
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Title of host publication | Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
Editors | Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu |
Pages | 111-116 |
Number of pages | 6 |
ISBN (Electronic) | 9781728118673 |
DOIs | |
State | Published - Nov 2019 |
Event | 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duration: Nov 18 2019 → Nov 21 2019 |
Publication series
Name | Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
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Conference
Conference | 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 |
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Country/Territory | United States |
City | San Diego |
Period | 11/18/19 → 11/21/19 |
Bibliographical note
Publisher Copyright:© 2019 IEEE.
Keywords
- anomaly detection
- isolation forest
- rare cells
- scRNA-seq
- transcriptomics
ASJC Scopus subject areas
- Biochemistry
- Biotechnology
- Molecular Medicine
- Modeling and Simulation
- Health Informatics
- Pharmacology (medical)
- Public Health, Environmental and Occupational Health