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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2023
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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. |
format | Online Article Text |
id | pubmed-10055660 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
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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