CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type

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CellBIC : bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type. / Kim, Junil; Stanescu, Diana E; Won, Kyoung Jae.

In: Nucleic Acids Research, Vol. 46, No. 21, e124, 2018, p. 1-8.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Kim, J, Stanescu, DE & Won, KJ 2018, 'CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type', Nucleic Acids Research, vol. 46, no. 21, e124, pp. 1-8. https://doi.org/10.1093/nar/gky698

APA

Kim, J., Stanescu, D. E., & Won, K. J. (2018). CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type. Nucleic Acids Research, 46(21), 1-8. [e124]. https://doi.org/10.1093/nar/gky698

Vancouver

Kim J, Stanescu DE, Won KJ. CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type. Nucleic Acids Research. 2018;46(21):1-8. e124. https://doi.org/10.1093/nar/gky698

Author

Kim, Junil ; Stanescu, Diana E ; Won, Kyoung Jae. / CellBIC : bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type. In: Nucleic Acids Research. 2018 ; Vol. 46, No. 21. pp. 1-8.

Bibtex

@article{f69ef7a360a14633b1727eb8310c93d3,
title = "CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type",
abstract = "Single-cell RNA sequencing (scRNA-seq) is a powerful tool to study heterogeneity and dynamic changes in cell populations. Clustering scRNA-seq is essential in identifying new cell types and studying their characteristics. We develop CellBIC (single Cell BImodal Clustering) to cluster scRNA-seq data based on modality in the gene expression distribution. Compared with classical bottom-up approaches that rely on a distance metric, CellBIC performs hierarchical clustering in a top-down manner. CellBIC outperformed the bottom-up hierarchical clustering approach and other recently developed clustering algorithms while maintaining the hierarchical structure of cells. Importantly, CellBIC identifies type 2 diabetes and age specific β cell signatures characterized by SIX3 and CDH2, respectively.",
author = "Junil Kim and Stanescu, {Diana E} and Won, {Kyoung Jae}",
year = "2018",
doi = "10.1093/nar/gky698",
language = "English",
volume = "46",
pages = "1--8",
journal = "Nucleic Acids Research",
issn = "0305-1048",
publisher = "Oxford University Press",
number = "21",

}

RIS

TY - JOUR

T1 - CellBIC

T2 - bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type

AU - Kim, Junil

AU - Stanescu, Diana E

AU - Won, Kyoung Jae

PY - 2018

Y1 - 2018

N2 - Single-cell RNA sequencing (scRNA-seq) is a powerful tool to study heterogeneity and dynamic changes in cell populations. Clustering scRNA-seq is essential in identifying new cell types and studying their characteristics. We develop CellBIC (single Cell BImodal Clustering) to cluster scRNA-seq data based on modality in the gene expression distribution. Compared with classical bottom-up approaches that rely on a distance metric, CellBIC performs hierarchical clustering in a top-down manner. CellBIC outperformed the bottom-up hierarchical clustering approach and other recently developed clustering algorithms while maintaining the hierarchical structure of cells. Importantly, CellBIC identifies type 2 diabetes and age specific β cell signatures characterized by SIX3 and CDH2, respectively.

AB - Single-cell RNA sequencing (scRNA-seq) is a powerful tool to study heterogeneity and dynamic changes in cell populations. Clustering scRNA-seq is essential in identifying new cell types and studying their characteristics. We develop CellBIC (single Cell BImodal Clustering) to cluster scRNA-seq data based on modality in the gene expression distribution. Compared with classical bottom-up approaches that rely on a distance metric, CellBIC performs hierarchical clustering in a top-down manner. CellBIC outperformed the bottom-up hierarchical clustering approach and other recently developed clustering algorithms while maintaining the hierarchical structure of cells. Importantly, CellBIC identifies type 2 diabetes and age specific β cell signatures characterized by SIX3 and CDH2, respectively.

U2 - 10.1093/nar/gky698

DO - 10.1093/nar/gky698

M3 - Journal article

VL - 46

SP - 1

EP - 8

JO - Nucleic Acids Research

JF - Nucleic Acids Research

SN - 0305-1048

IS - 21

M1 - e124

ER -

ID: 200859397