A general and flexible method for signal extraction from single-cell RNA-seq data

Risso, Davide; Perraudeau, Fanny; Gribkova, Svetlana; Dudoit, Sandrine; Vert, Jean-Philippe

doi:10.1038/s41467-017-02554-5

Cited by 612 publications

(665 citation statements)

References 54 publications

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“…pCMF models both dropout events and the mean-variance dependence resulting from the count nature of scRNAseq data [31]. ZINB-WaVE incorporates additional gene-level and sample-level covariates for more accurate DR [32]. Finally, several deep learning-based DR methods have recently been developed to enable scalable and effective computation in large-scale scRNAseq data, including data that are collected by 10X Genomics techniques [33] and/or from large consortium studies such as Human Cell Atlas (HCA) [34,35].…”

Section: Introductionmentioning

confidence: 99%

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Sun

Zhu

et al. 2019

Preprint

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Background: Dimensionality reduction (DR) is an indispensable analytic component for many areas of single cell RNA sequencing (scRNAseq) data analysis. Proper DR can allow for effective noise removal and facilitate many downstream analyses that include cell clustering and lineage reconstruction. Unfortunately, despite the critical importance of DR in scRNAseq analysis and the vast number of DR methods developed for scRNAseq studies, however, few comprehensive comparison studies have been performed to evaluate the effectiveness of different DR methods in scRNAseq.Results: Here, we aim to fill this critical knowledge gap by providing a comparative evaluation of a variety of commonly used DR methods for scRNAseq studies. Specifically, we compared 18 different DR methods on 30 publicly available scRNAseq data sets that cover a range of sequencing techniques and sample sizes. We evaluated the performance of different DR methods for neighborhood preserving in terms of their ability to recover features of the original expression matrix, and for cell clustering and lineage reconstruction in terms of their accuracy and robustness. We also evaluated the computational scalability of different DR methods by recording their computational cost. Conclusions:Based on the comprehensive evaluation results, we provide important guidelines for choosing DR methods for scRNAseq data analysis. We also provide all analysis scripts used in the present study at www.xzlab.org/reproduce.html. Together, we hope that our results will serve as an important practical reference for practitioners to choose DR methods in the field of scRNAseq analysis. our results can serve as an important guideline for practitioners to choose DR methods in the field of scRNAseq analysis.

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Section: Introductionmentioning

confidence: 99%

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Sun

Zhu

et al. 2019

Preprint

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“…However, choosing genes solely based on log‐normalized single‐cell variance fails to account for the mean‐variance relationship that is inherent to single‐cell RNA‐seq . Variance‐stabilizing transformation can be applied to transform both lowly and highly expressed genes onto a common scale, thus eliminating the effect of sequencing depth variation …”

Section: Computational Analysis Of Scrna‐seq Datamentioning

confidence: 99%

“…23 Variance-stabilizing transformation can be applied to transform both lowly and highly expressed genes onto a common scale, thus eliminating the effect of sequencing depth variation. 24 Dimension reduction is widely used in machine learning that project the high dimensional data to lower dimensional space, and preserve important features of the original data. Principal component analysis (PCA), T-distributed stochastic neighbor embedding (tSNE), 25 Diffusion maps (DM) 26 and Uniform Manifold Approximation and Projection (UMAP) 27 are widely used dimension reduction methods.…”

Section: Feature Selection and Dimension Reductionmentioning

confidence: 99%

Dissecting the human immune system with single cell RNA sequencing technology

Liu

et al. 2019

Journal of Leukocyte Biology

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Single‐cell RNA sequencing (scRNA‐seq) is a powerful new technology allowing the analysis of transcriptomes from individual cell and is ideally suited to dissect immune cell heterogeneity. ScRNA‐seq has already been applied to identify novel immune cell subsets, elaborate cellular differentiation trajectories, and elucidate immunopathogenic mechanisms. Here, we briefly discuss the recent progresses and challenges in the scRNA‐seq technology including the workflow, recent applications in immunology, and potential hurdles that need to be overcome. This review will highlight how single cell technology promotes our understanding of human immunology.

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“…As scRNA-seq data can be approximated by negative binomial (NB) or zeroinflated NB (ZINB) distribution 16,17 , we considered the use of the statistic, (expression entropydifferential entropy of expression distribution, as defined in Methods), to capture the degree of disorder or randomness of gene expression. Notably, we observed a strong relationship between and the mean expression level ( ) of genes, thus forming the basis for our expression entropy model (model, Fig.…”

Section: Expression Entropy Model Enables Sensitive and Accurate Idenmentioning

confidence: 99%

ROGUE: an entropy-based universal metric for assessing the purity of single cell population

Liu

et al. 2019

Preprint

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Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrated that our ROGUE metric is generalizable across datasets, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast and B cell datasets, we identified additional subtypes and demonstrated the application of ROGUE-guided analyses to detect true signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas. Z.Z. conceived this study. C.L. and B.L. designed the -model. B.L. introduced the algorithm of ROGUE, performed the benchmark testing, analyzed the data and developed the R package. Z.L. and X.R. assisted with method development. B.L., C.L. and Z.Z. wrote the manuscript with all the authors' inputs.

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A general and flexible method for signal extraction from single-cell RNA-seq data

Cited by 612 publications

References 54 publications

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Accuracy, Robustness and Scalability of Dimensionality Reduction Methods for Single Cell RNAseq Analysis

Dissecting the human immune system with single cell RNA sequencing technology

ROGUE: an entropy-based universal metric for assessing the purity of single cell population

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