TedSim: temporal dynamics simulation of single cell RNA-sequencing data and cell division history

Pan, Xinhai; Li, Hechen; Zhang, X

doi:10.1101/2021.06.21.449283

Cited by 2 publications

(2 citation statements)

References 48 publications

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“…Salvador-Martínez et al [58] proposed a simulator for lineage barcode data that, does not generate gene expression data. A recent method, TedSim [59], is capable of outputting combined readouts and can be used to study tools for either or both data types, including more genuine investigation of trajectory inference methods.…”

Section: Discussionmentioning

confidence: 99%

The shaky foundations of simulating single-cell RNA sequencing data

et al. 2023

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Background With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq) datasets, the number of computational tools to analyze aspects of the generated data has grown rapidly. As a result, there is a recurring need to demonstrate whether newly developed methods are truly performant—on their own as well as in comparison to existing tools. Benchmark studies aim to consolidate the space of available methods for a given task and often use simulated data that provide a ground truth for evaluations, thus demanding a high quality standard results credible and transferable to real data. Results Here, we evaluated methods for synthetic scRNA-seq data generation in their ability to mimic experimental data. Besides comparing gene- and cell-level quality control summaries in both one- and two-dimensional settings, we further quantified these at the batch- and cluster-level. Secondly, we investigate the effect of simulators on clustering and batch correction method comparisons, and, thirdly, which and to what extent quality control summaries can capture reference-simulation similarity. Conclusions Our results suggest that most simulators are unable to accommodate complex designs without introducing artificial effects, they yield over-optimistic performance of integration and potentially unreliable ranking of clustering methods, and it is generally unknown which summaries are important to ensure effective simulation-based method comparisons.

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Section: Discussionmentioning

confidence: 99%

The shaky foundations of simulating single-cell RNA sequencing data

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Salvador-Martínez et al [57] have proposed a simulator for lineage barcode data that, however, does not generate gene expression data. A recent method, TedSim [58] , is capable of outputting combined readouts, and can be used to study tools for either or both data types, including more genuine investigation of trajectory inference methods.…”

Section: Discussionmentioning

confidence: 99%

Built on sand: the shaky foundations of simulating single-cell RNA sequencing data

Crowell

Leonardo

Soneson

et al. 2021

Preprint

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With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq) datasets, the number of computational tools to analyse aspects of the generated data has grown rapidly. As a result, there is a recurring need to demonstrate whether newly developed methods are truly performant – on their own as well as in comparison to existing tools. Benchmark studies aim to consolidate the space of available methods for a given task, and often use simulated data that provide a ground truth for evaluations. Thus, demanding a high quality standard for synthetically generated data is critical to make simulation study results credible and transferable to real data.Here, we evaluated methods for synthetic scRNA-seq data generation in their ability to mimic experimental data. Besides comparing gene- and cell-level quality control summaries in both one- and two-dimensional settings, we further quantified these at the batch- and cluster-level. Secondly, we investigate the effect of simulators on clustering and batch correction method comparisons, and, thirdly, which and to what extent quality control summaries can capture reference-simulation similarity.Our results suggest that most simulators are unable to accommodate complex designs without introducing artificial effects; they yield over-optimistic performance of integration, and potentially unreliable ranking of clustering methods; and, it is generally unknown which summaries are important to ensure effective simulation-based method comparisons.

show abstract

TedSim: temporal dynamics simulation of single cell RNA-sequencing data and cell division history

Cited by 2 publications

References 48 publications

The shaky foundations of simulating single-cell RNA sequencing data

The shaky foundations of simulating single-cell RNA sequencing data

Built on sand: the shaky foundations of simulating single-cell RNA sequencing data

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