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

Sun, Tianyi; Song, Dongyuan; Li, Jingyi Jessica

doi:10.1101/2020.11.17.387795

Cited by 24 publications

(51 citation statements)

References 132 publications

(208 reference statements)

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“…The source code and data for reproducing the results are available at https://doi.org/10.5281/zenodo.4011311 [119]. Both the R package and the source code are under the MIT license.…”

Section: Supplementary Informationmentioning

confidence: 99%

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

Sun

Song

2021

Genome Biol

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A 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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“…The source code and data for reproducing the results are available at https://doi.org/10.5281/zenodo.4011311 [119]. Both the R package and the source code are under the MIT license.…”

Section: Supplementary Informationmentioning

confidence: 99%

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

Sun

Song

2021

Genome Biol

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“…In the first case study, we use the Zheng8 dataset (measured by the 10x protocol) as the reference dataset. To generate the pseudo targeted gene profiling data, we use a new single-cell gene expression simulator that captures gene correlations, scDesign2 [39], to generate data with a 100-time higher per-cell sequencing depth. In the second case study, we use the PBMC10x dataset (measured by 10x protocol) as the reference dataset, and we use PBMCSmartseq (measured by Smart-Seq2) as the pseudo targeted gene profiling data because Smart-Seq2 has a higher pergene sequencing depth than 10x does.…”

Section: Resultsmentioning

confidence: 99%

“…We use two datasets, Zheng8 and PBMC10x, as the reference scRNA-seq datasets. For Zheng8 dataset, we first use scDesign2 [39] to learn the underlying parameters, and then simulate a new dataset with same genes and cell types but 100 times higher sequencing depth compared to the Zheng8 dataset. For PBMC10x dataset, we use the PBMCSmartSeq dataset, which measures the exact same example and contains all genes measured in PBMC10x.…”

Section: Supplementary Materialsmentioning

confidence: 99%

scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

Song

Hemminger

et al. 2021

Preprint

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Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of indi- vidual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity, and extra (e.g., spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Here we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and cell-type annotation on targeted gene profiling data.

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“…We compared Clipper (Additional File 1: Section S7.4) with edgeR [4], MAST [56], Monocle3 [57], the two-sample t test, and the Wilcoxon rank-sum test (Additional File 1: Section S5.4), five methods that are either popular or reported to have comparatively top performance from a previous benchmark study [58]. To verify the FDR control, we used scDesign2, a flexible probabilistic simulator, to generate scRNA-seq count data with known true DEGs [59]. scDesign2 offers three key advantages that en-able the generation of realistic semi-synthetic scRNA-seq count data: (1) it captures distinct marginal distributions of different genes; (2) it preserves gene-gene correlations; (3) it adapts to various scRNA-seq protocols.…”

Section: Resultsmentioning

confidence: 99%

Clipper: p-value-free FDR control on high-throughput data from two conditions

Chen

Song³

et al. 2020

Preprint

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High-throughput biological data analysis commonly involves the identification of “interesting” features (e.g., genes, genomic regions, and proteins), whose values differ between two conditions, from numerous features measured simultaneously. To ensure the reliability of such analysis, the most widely-used criterion is the false discovery rate (FDR), the expected proportion of uninteresting features among the identified ones. Existing bioinformatics tools primarily control the FDR based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions, two requirements that are often unmet in biological studies. To address this issue, we propose Clipper, a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper is applicable to identifying both enriched and differential features from high-throughput biological data of diverse types. In comprehensive simulation and real-data benchmarking, Clipper outperforms existing generic FDR control methods and specific bioinformatics tools designed for various tasks, including peak calling from ChIP-seq data, differentially expressed gene identification from RNA-seq data, differentially interacting chromatin region identification from Hi-C data, and peptide identification from mass spectrometry data. Notably, our benchmarking results for peptide identification are based on the first mass spectrometry data standard that has a realistic dynamic range. Our results demonstrate Clipper’s flexibility and reliability for FDR control, as well as its broad applications in high-throughput data analysis.

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

Cited by 24 publications

References 132 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

scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

Clipper: p-value-free FDR control on high-throughput data from two conditions

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