Cargando…

A multiresolution framework to characterize single-cell state landscapes

Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although many methods and approaches exist, identifying cell states and their underlying topology is still a major challenge. Here, we introduce the concept of multiresolution cell-state decomposition a...

Descripción completa

Detalles Bibliográficos
Autores principales: Mohammadi, Shahin, Davila-Velderrain, Jose, Kellis, Manolis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588427/
https://www.ncbi.nlm.nih.gov/pubmed/33106496
http://dx.doi.org/10.1038/s41467-020-18416-6
_version_ 1783600369222287360
author Mohammadi, Shahin
Davila-Velderrain, Jose
Kellis, Manolis
author_facet Mohammadi, Shahin
Davila-Velderrain, Jose
Kellis, Manolis
author_sort Mohammadi, Shahin
collection PubMed
description Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although many methods and approaches exist, identifying cell states and their underlying topology is still a major challenge. Here, we introduce the concept of multiresolution cell-state decomposition as a practical approach to simultaneously capture both fine- and coarse-grain patterns of variability. We implement this concept in ACTIONet, a comprehensive framework that combines archetypal analysis and manifold learning to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet provides a robust, reproducible, and highly interpretable single-cell analysis platform that couples dominant pattern discovery with a corresponding structural representation of the cell state landscape. Using multiple synthetic and real data sets, we demonstrate ACTIONet’s superior performance relative to existing alternatives. We use ACTIONet to integrate and annotate cells across three human cortex data sets. Through integrative comparative analysis, we define a consensus vocabulary and a consistent set of gene signatures discriminating against the transcriptomic cell types and subtypes of the human prefrontal cortex.
format Online
Article
Text
id pubmed-7588427
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-75884272020-11-10 A multiresolution framework to characterize single-cell state landscapes Mohammadi, Shahin Davila-Velderrain, Jose Kellis, Manolis Nat Commun Article Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although many methods and approaches exist, identifying cell states and their underlying topology is still a major challenge. Here, we introduce the concept of multiresolution cell-state decomposition as a practical approach to simultaneously capture both fine- and coarse-grain patterns of variability. We implement this concept in ACTIONet, a comprehensive framework that combines archetypal analysis and manifold learning to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet provides a robust, reproducible, and highly interpretable single-cell analysis platform that couples dominant pattern discovery with a corresponding structural representation of the cell state landscape. Using multiple synthetic and real data sets, we demonstrate ACTIONet’s superior performance relative to existing alternatives. We use ACTIONet to integrate and annotate cells across three human cortex data sets. Through integrative comparative analysis, we define a consensus vocabulary and a consistent set of gene signatures discriminating against the transcriptomic cell types and subtypes of the human prefrontal cortex. Nature Publishing Group UK 2020-10-26 /pmc/articles/PMC7588427/ /pubmed/33106496 http://dx.doi.org/10.1038/s41467-020-18416-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Mohammadi, Shahin
Davila-Velderrain, Jose
Kellis, Manolis
A multiresolution framework to characterize single-cell state landscapes
title A multiresolution framework to characterize single-cell state landscapes
title_full A multiresolution framework to characterize single-cell state landscapes
title_fullStr A multiresolution framework to characterize single-cell state landscapes
title_full_unstemmed A multiresolution framework to characterize single-cell state landscapes
title_short A multiresolution framework to characterize single-cell state landscapes
title_sort multiresolution framework to characterize single-cell state landscapes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588427/
https://www.ncbi.nlm.nih.gov/pubmed/33106496
http://dx.doi.org/10.1038/s41467-020-18416-6
work_keys_str_mv AT mohammadishahin amultiresolutionframeworktocharacterizesinglecellstatelandscapes
AT davilavelderrainjose amultiresolutionframeworktocharacterizesinglecellstatelandscapes
AT kellismanolis amultiresolutionframeworktocharacterizesinglecellstatelandscapes
AT mohammadishahin multiresolutionframeworktocharacterizesinglecellstatelandscapes
AT davilavelderrainjose multiresolutionframeworktocharacterizesinglecellstatelandscapes
AT kellismanolis multiresolutionframeworktocharacterizesinglecellstatelandscapes