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scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks

Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to sim...

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Autores principales: Li, Hechen, Zhang, Ziqi, Squires, Michael, Chen, Xi, Zhang, Xiuwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055660/
https://www.ncbi.nlm.nih.gov/pubmed/36993284
http://dx.doi.org/10.21203/rs.3.rs-2675530/v1
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author Li, Hechen
Zhang, Ziqi
Squires, Michael
Chen, Xi
Zhang, Xiuwei
author_facet Li, Hechen
Zhang, Ziqi
Squires, Michael
Chen, Xi
Zhang, Xiuwei
author_sort Li, Hechen
collection PubMed
description Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, while also incorporating technical noises. Moreover, it allows users to adjust each factor’s effect easily. We validated scMultiSim’s simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including cell clustering and trajectory inference, multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.
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spelling pubmed-100556602023-03-30 scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks Li, Hechen Zhang, Ziqi Squires, Michael Chen, Xi Zhang, Xiuwei Res Sq Article Simulated single-cell data is essential for designing and evaluating computational methods in the absence of experimental ground truth. Existing simulators typically focus on modeling one or two specific biological factors or mechanisms that affect the output data, which limits their capacity to simulate the complexity and multi-modality in real data. Here, we present scMultiSim, an in silico simulator that generates multi-modal single-cell data, including gene expression, chromatin accessibility, RNA velocity, and spatial cell locations while accounting for the relationships between modalities. scMultiSim jointly models various biological factors that affect the output data, including cell identity, within-cell gene regulatory networks (GRNs), cell-cell interactions (CCIs), and chromatin accessibility, while also incorporating technical noises. Moreover, it allows users to adjust each factor’s effect easily. We validated scMultiSim’s simulated biological effects and demonstrated its applications by benchmarking a wide range of computational tasks, including cell clustering and trajectory inference, multi-modal and multi-batch data integration, RNA velocity estimation, GRN inference and CCI inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks. American Journal Experts 2023-03-15 /pmc/articles/PMC10055660/ /pubmed/36993284 http://dx.doi.org/10.21203/rs.3.rs-2675530/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Li, Hechen
Zhang, Ziqi
Squires, Michael
Chen, Xi
Zhang, Xiuwei
scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks
title scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks
title_full scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks
title_fullStr scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks
title_full_unstemmed scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks
title_short scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks
title_sort scmultisim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055660/
https://www.ncbi.nlm.nih.gov/pubmed/36993284
http://dx.doi.org/10.21203/rs.3.rs-2675530/v1
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