dyngen: a multi-modal simulator for spearheading new single-cell omics analyses

Cannoodt, Robrecht; Saelens, Wouter; Deconinck, Louise; Saeys, Yvan

doi:10.1101/2020.02.06.936971

Cited by 23 publications

(33 citation statements)

References 43 publications

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“…However, FiRE is a more robust method that it can successfully run at all sequencing depths and cell numbers, while GiniClust2 fails when the cell number is too small or too large (GiniClust3 may have addressed this large-cell-number issue [87]). This finding is consistent with the methodological difference between the two methods: Path [65], dyngen [91], and PROSSTT [64]. Another note is that scDesign2 does not generate synthetic cells based on outlier cells that do not cluster well with any cells in well-formed clusters.…”

supporting

confidence: 79%

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

Sun

Song

2020

Preprint

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In the burgeoning field of single-cell transcriptomics, a pressing challenge is to benchmark various experimental protocols and numerous computational methods in an unbiased manner. Although dozens of simulators have been developed for single-cell RNA-seq (scRNA-seq) data, they lack the capacity to simultaneously achieve all the three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill in this gap, here we propose scDesign2, an interpretable simulator that achieves all the three goals and generates high-fidelity synthetic data for multiple scRNA-seq protocols and other single-cell gene expression count-based technologies. Compared with existing simulators, scDesign2 is advantageous in its transparent use of probabilistic models and is unique in its ability to capture gene correlations via copula. We verify that scDesign2 generates more realistic synthetic data for four scRNA-seq protocols (10x Genomics, CEL-Seq2, Fluidigm C1, and Smart-Seq2) and two single-cell spatial transcriptomics protocols (MERFISH and pciSeq) than existing simulators do. Under two typical computational tasks, cell clustering and rare cell type detection, we demonstrate that scDesign2 provides informative guidance on deciding the optimal sequencing depth and cell number in single-cell RNA-seq experimental design, and that scDesign2 can effectively benchmark computational methods under varying sequencing depths and cell numbers. With these advantages, scDesign2 is a powerful tool for single-cell researchers to design experiments, develop computational methods, and choose appropriate methods for specific data analysis needs.

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supporting

confidence: 79%

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

Sun

Song

2020

Preprint

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“…For synthetic datasets, we consider two different simulation approaches. One simulator we use is dyngen (Cannoodt et al, 2020), a multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is also used in Saelens et al (2019) and provides a delicate way to generate scRNA-seq data starting from gene regulation and transcriptional factors.…”

Section: Datasetsmentioning

confidence: 99%

Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior

Gao

Wang

2020

Preprint

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Trajectory inference methods analyze thousands of cells from single-cell sequencing technologies and computationally infer their developmental trajectories. Though many tools have been developed for trajectory inference, most of them lack a coherent statistical model and reliable uncertainty quantification. In this paper, we present VITAE, a probabilistic method combining a latent hierarchical mixture model with variational autoencoders to infer trajectories from posterior approximations. VITAE is computationally scalable and can adjust for confounding covariates to integrate multiple datasets. We show that VITAE outperforms other state-of-the-art trajectory inference methods on both real and synthetic data under various trajectory topologies. We also apply VITAE to jointly analyze two single-cell RNA sequencing datasets on mouse neocortex. Our results suggest that VITAE can successfully uncover a shared developmental trajectory of the projection neurons and reliably order cells from both datasets along the inferred trajectory.

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“…Dyngen 10 and SERGIO 11 are able to simulate both spliced and unspliced mRNA counts of cells in a continuous dynamic process, but they each has its own limitations in practice. Dyngen provides predefined trajectory structure types in the package, such as bifurcating or cycle, but it is not flexible enough for more customized trajectory structure where user need to specify the exact developmental tree structure and the length of each branch.…”

Section: Introductionmentioning

confidence: 99%

VeloSim: Simulating single cell gene-expression and RNA velocity

Zhang

2021

Preprint

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The availability of high throughput single-cell RNA-Sequencing data allows researchers to study the molecular mechanisms that drive the temporal dynamics of cells during differentiation or development. Recent computational methods that build upon single-cell sequencing technology, such as trajectory inference or RNA-velocity estimation, provide a way for researchers to analyze the state of each cell during a continuous dynamic process. However, with the surge of such computational methods, there is still a lack of simulators that can model the cell temporal dynamics, and provide ground truth data to benchmark the computational methods. Hereby we present VeloSim, a simulation software that can simulate the gene-expression kinetics in cells along continuous trajectories. VeloSim is able to take any trajectory structure composed of basic elements including ''linear'' and ''cycle'' as input, and outputs unspliced mRNA count matrix, spliced mRNA count matrix, cell pseudo-time and true RNA velocity of the cells. We demonstrate how VeloSim can be used to benchmark trajectory inference and RNA-velocity estimation methods with different amounts of biological and technical variation within the datasets. VeloSim is implemented into an R package available at https://github.com/PeterZZQ/VeloSim.

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dyngen: a multi-modal simulator for spearheading new single-cell omics analyses

Cited by 23 publications

References 43 publications

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured

Joint Trajectory Inference for Single-cell Genomics Using Deep Learning with a Mixture Prior

VeloSim: Simulating single cell gene-expression and RNA velocity

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