Cargando…
Model-based branching point detection in single-cell data by K-branches clustering
MOTIVATION: The identification of heterogeneities in cell populations by utilizing single-cell technologies such as single-cell RNA-Seq, enables inference of cellular development and lineage trees. Several methods have been proposed for such inference from high-dimensional single-cell data. They typ...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860029/ https://www.ncbi.nlm.nih.gov/pubmed/28582478 http://dx.doi.org/10.1093/bioinformatics/btx325 |
_version_ | 1783307937291173888 |
---|---|
author | Chlis, Nikolaos K Wolf, F Alexander Theis, Fabian J |
author_facet | Chlis, Nikolaos K Wolf, F Alexander Theis, Fabian J |
author_sort | Chlis, Nikolaos K |
collection | PubMed |
description | MOTIVATION: The identification of heterogeneities in cell populations by utilizing single-cell technologies such as single-cell RNA-Seq, enables inference of cellular development and lineage trees. Several methods have been proposed for such inference from high-dimensional single-cell data. They typically assign each cell to a branch in a differentiation trajectory. However, they commonly assume specific geometries such as tree-like developmental hierarchies and lack statistically sound methods to decide on the number of branching events. RESULTS: We present K-Branches, a solution to the above problem by locally fitting half-lines to single-cell data, introducing a clustering algorithm similar to K-Means. These halflines are proxies for branches in the differentiation trajectory of cells. We propose a modified version of the GAP statistic for model selection, in order to decide on the number of lines that best describe the data locally. In this manner, we identify the location and number of subgroups of cells that are associated with branching events and full differentiation, respectively. We evaluate the performance of our method on single-cell RNA-Seq data describing the differentiation of myeloid progenitors during hematopoiesis, single-cell qPCR data of mouse blastocyst development, single-cell qPCR data of human myeloid monocytic leukemia and artificial data. AVAILABILITY AND IMPLEMENTATION: An R implementation of K-Branches is freely available at https://github.com/theislab/kbranches. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-5860029 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-58600292018-03-23 Model-based branching point detection in single-cell data by K-branches clustering Chlis, Nikolaos K Wolf, F Alexander Theis, Fabian J Bioinformatics Original Papers MOTIVATION: The identification of heterogeneities in cell populations by utilizing single-cell technologies such as single-cell RNA-Seq, enables inference of cellular development and lineage trees. Several methods have been proposed for such inference from high-dimensional single-cell data. They typically assign each cell to a branch in a differentiation trajectory. However, they commonly assume specific geometries such as tree-like developmental hierarchies and lack statistically sound methods to decide on the number of branching events. RESULTS: We present K-Branches, a solution to the above problem by locally fitting half-lines to single-cell data, introducing a clustering algorithm similar to K-Means. These halflines are proxies for branches in the differentiation trajectory of cells. We propose a modified version of the GAP statistic for model selection, in order to decide on the number of lines that best describe the data locally. In this manner, we identify the location and number of subgroups of cells that are associated with branching events and full differentiation, respectively. We evaluate the performance of our method on single-cell RNA-Seq data describing the differentiation of myeloid progenitors during hematopoiesis, single-cell qPCR data of mouse blastocyst development, single-cell qPCR data of human myeloid monocytic leukemia and artificial data. AVAILABILITY AND IMPLEMENTATION: An R implementation of K-Branches is freely available at https://github.com/theislab/kbranches. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2017-10-15 2017-06-05 /pmc/articles/PMC5860029/ /pubmed/28582478 http://dx.doi.org/10.1093/bioinformatics/btx325 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Chlis, Nikolaos K Wolf, F Alexander Theis, Fabian J Model-based branching point detection in single-cell data by K-branches clustering |
title | Model-based branching point detection in single-cell data by K-branches clustering |
title_full | Model-based branching point detection in single-cell data by K-branches clustering |
title_fullStr | Model-based branching point detection in single-cell data by K-branches clustering |
title_full_unstemmed | Model-based branching point detection in single-cell data by K-branches clustering |
title_short | Model-based branching point detection in single-cell data by K-branches clustering |
title_sort | model-based branching point detection in single-cell data by k-branches clustering |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5860029/ https://www.ncbi.nlm.nih.gov/pubmed/28582478 http://dx.doi.org/10.1093/bioinformatics/btx325 |
work_keys_str_mv | AT chlisnikolaosk modelbasedbranchingpointdetectioninsinglecelldatabykbranchesclustering AT wolffalexander modelbasedbranchingpointdetectioninsinglecelldatabykbranchesclustering AT theisfabianj modelbasedbranchingpointdetectioninsinglecelldatabykbranchesclustering |