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

AbstractA pressing challenge in single-cell transcriptomics is to benchmark experimental protocols and computational methods. A solution is to use computational simulators, but existing simulators cannot simultaneously achieve three goals: preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths. To fill this gap, we propose scDesign2, a transparent simulator that achieves all three goals and generates high-fidelity synthetic data for multiple single-cell gene expression count-based technologies. In particular, scDesign2 is advantageous in its transparent use of probabilistic models and its ability to capture gene correlations via copulas.

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

10.1101/2020.11.17.387795 ◽

2020 ◽

Author(s):

Tianyi Sun ◽

Dongyuan Song ◽

Wei Vivian Li ◽

Jingyi Jessica Li

Keyword(s):

Gene Expression ◽

Single Cell ◽

Computational Methods ◽

Probabilistic Models ◽

Synthetic Data ◽

Cell Number ◽

High Fidelity ◽

Rna Seq ◽

Cell Gene Expression ◽

Cell Gene

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

Genome Biology ◽

10.1186/s13059-021-02394-z ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Tianyi Sun ◽

Dongyuan Song ◽

Wei Vivian Li ◽

Jingyi Jessica Li

Keyword(s):

Gene Expression ◽

Single Cell ◽

Count Data ◽

High Fidelity ◽

Cell Gene Expression ◽

Cell Gene

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BGP: Branched Gaussian processes for identifying gene-specific branching dynamics in single cell data

10.1101/166868 ◽

2017 ◽

Cited By ~ 3

Author(s):

Alexis Boukouvalas ◽

James Hensman ◽

Magnus Rattray

Keyword(s):

Gene Expression ◽

Single Cell ◽

Prior Information ◽

Synthetic Data ◽

Parametric Model ◽

Credible Region ◽

Cell Gene Expression ◽

Probabilistic Nature ◽

Cell Data ◽

Cell Gene

AbstractHigh-throughput single-cell gene expression experiments can be used to uncover branching dynamics in cell populations undergoing differentiation through use of pseudotime methods. We develop the branching Gaussian process (BGP), a non-parametric model that is able to identify branching dynamics for individual genes and provides an estimate of branching times for each gene with an associated credible region. We demonstrate the effectiveness of our method on both synthetic data and a published single-cell gene expression hematopoiesis study. The method requires prior information about pseudotime and global cellular branching for each cell but the probabilistic nature of the method means that it is robust to errors in these global branch labels and can be used to discover early branching genes which diverge before the inferred global cell branching. The code is open-source and available at https://github.com/ManchesterBioinference/BranchedGP.

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