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Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

BACKGROUND: Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the c...

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Autores principales: Ranek, Jolene S., Stanley, Natalie, Purvis, Jeremy E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9442962/
https://www.ncbi.nlm.nih.gov/pubmed/36064614
http://dx.doi.org/10.1186/s13059-022-02749-0
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author Ranek, Jolene S.
Stanley, Natalie
Purvis, Jeremy E.
author_facet Ranek, Jolene S.
Stanley, Natalie
Purvis, Jeremy E.
author_sort Ranek, Jolene S.
collection PubMed
description BACKGROUND: Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics. RESULTS: Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark ten integration approaches on ten datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods. CONCLUSIONS: This work illustrates how integrated temporal gene expression modalities may be leveraged for predicting cellular trajectories and sample-associated perturbation and disease phenotypes. Additionally, this study provides users with practical recommendations for task-specific integration of single-cell gene expression modalities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02749-0.
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spelling pubmed-94429622022-09-06 Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction Ranek, Jolene S. Stanley, Natalie Purvis, Jeremy E. Genome Biol Research BACKGROUND: Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics. RESULTS: Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark ten integration approaches on ten datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods. CONCLUSIONS: This work illustrates how integrated temporal gene expression modalities may be leveraged for predicting cellular trajectories and sample-associated perturbation and disease phenotypes. Additionally, this study provides users with practical recommendations for task-specific integration of single-cell gene expression modalities. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02749-0. BioMed Central 2022-09-05 /pmc/articles/PMC9442962/ /pubmed/36064614 http://dx.doi.org/10.1186/s13059-022-02749-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ranek, Jolene S.
Stanley, Natalie
Purvis, Jeremy E.
Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
title Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
title_full Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
title_fullStr Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
title_full_unstemmed Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
title_short Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
title_sort integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9442962/
https://www.ncbi.nlm.nih.gov/pubmed/36064614
http://dx.doi.org/10.1186/s13059-022-02749-0
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