DoRC: Discovery of rare cells from ultra-large scRNA-seq data

Xiang Chen; Fang Xiang Wu; Jin Chen; Min Li

doi:10.1109/BIBM47256.2019.8983250

DoRC: Discovery of rare cells from ultra-large scRNA-seq data

Xiang Chen
, Fang Xiang Wu
, Jin Chen
, Min Li

Internal Medicine

Producción científica: Conference contribution › revisión exhaustiva

3 Citas (Scopus)

Resumen

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.

Idioma original	English
Título de la publicación alojada	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Editores	Illhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
Páginas	111-116
Número de páginas	6
ISBN (versión digital)	9781728118673
DOI	https://doi.org/10.1109/BIBM47256.2019.8983250
Estado	Published - nov 2019
Evento	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States Duración: nov 18 2019 → nov 21 2019

Serie de la publicación

Nombre	Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference	2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
País/Territorio	United States
Ciudad	San Diego
Período	11/18/19 → 11/21/19

Nota bibliográfica

Publisher Copyright:
© 2019 IEEE.

Financiación

We would like to thank Dr Aashi Jindal for fruitful discussions. This work is supported in part by the National Natural Science Foundation of China under Grants (No.61622213, No.61732009), the 111 Project (No.B18059) and the Hunan Provincial Science and Technology Program (2018WK4001).

Financiadores	Número del financiador
National Natural Science Foundation of China (NSFC)	61622213, 61732009
Hunan Provincial Science and Technology Department	2018WK4001
Higher Education Discipline Innovation Project

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

Good health and well being

ASJC Scopus subject areas

Biochemistry
Biotechnology
Molecular Medicine
Modeling and Simulation
Health Informatics
Pharmacology (medical)
Public Health, Environmental and Occupational Health

Acceder al documento

10.1109/BIBM47256.2019.8983250

Otros archivos y enlaces

Citar esto

Chen, X., Wu, F. X., Chen, J., & Li, M. (2019). DoRC: Discovery of rare cells from ultra-large scRNA-seq data. En I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 111-116). Artículo 8983250 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). https://doi.org/10.1109/BIBM47256.2019.8983250

Chen, Xiang ; Wu, Fang Xiang ; Chen, Jin et al. / DoRC : Discovery of rare cells from ultra-large scRNA-seq data. Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. editor / Illhoi Yoo ; Jinbo Bi ; Xiaohua Tony Hu. 2019. pp. 111-116 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

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title = "DoRC: Discovery of rare cells from ultra-large scRNA-seq data",

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.",

keywords = "anomaly detection, isolation forest, rare cells, scRNA-seq, transcriptomics",

author = "Xiang Chen and Wu, \{Fang Xiang\} and Jin Chen and Min Li",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 ; Conference date: 18-11-2019 Through 21-11-2019",

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doi = "10.1109/BIBM47256.2019.8983250",

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Chen, X, Wu, FX, Chen, J & Li, M 2019, DoRC: Discovery of rare cells from ultra-large scRNA-seq data. En I Yoo, J Bi & XT Hu (eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019., 8983250, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, pp. 111-116, 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, San Diego, United States, 11/18/19. https://doi.org/10.1109/BIBM47256.2019.8983250

DoRC: Discovery of rare cells from ultra-large scRNA-seq data. / Chen, Xiang; Wu, Fang Xiang; Chen, Jin et al.
Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. ed. / Illhoi Yoo; Jinbo Bi; Xiaohua Tony Hu. 2019. p. 111-116 8983250 (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019).

Producción científica: Conference contribution › revisión exhaustiva

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AU - Wu, Fang Xiang

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PY - 2019/11

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N2 - 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.

AB - 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.

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Chen X, Wu FX, Chen J, Li M. DoRC: Discovery of rare cells from ultra-large scRNA-seq data. En Yoo I, Bi J, Hu XT, editores, Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019. 2019. p. 111-116. 8983250. (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). doi: 10.1109/BIBM47256.2019.8983250

DoRC: Discovery of rare cells from ultra-large scRNA-seq data

Resumen

Serie de la publicación

Conference

Nota bibliográfica

Financiación

ODS de las Naciones Unidas

ASJC Scopus subject areas

Acceder al documento

Otros archivos y enlaces

Huella

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