DEsingle for detecting three types of differential expression in single-cell RNA-seq data

Miao, Zhun; Deng, Ke; Wang, Xiaowo; Zhang, Xuegong

doi:10.1093/bioinformatics/bty332

Cited by 227 publications

(210 citation statements)

References 43 publications

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“…Thanks to its data-adaptive nature and non-linearity assumptions, it successfully manages to provide reliable, informative LFC estimates, in addition to pertinent DE gene candidates for scRNA-seq data. Notably, such a framework is flexible and can be extended to other types of hypotheses such as differential variance analysis [20,21].…”

Section: Detection Of Differentially Expressed Genesmentioning

confidence: 99%

Deep Generative Models for Detecting Differential Expression in Single Cells

Boyeau

Lopez

Regier

et al. 2019

Preprint

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Detecting differentially expressed genes is important for characterizing subpopulations of cells. However, in scRNA-seq data, nuisance variation due to technical factors like sequencing depth and RNA capture efficiency obscures the underlying biological signal. First, we show that deep generative models, which combined Bayesian statistics and deep neural networks, better estimate the log-fold-change in gene expression levels between subpopulations of cells. Second, we use Bayesian decision theory to detect differentially expressed genes while controlling the false discovery rate. Our experiments on simulated and real datasets show that our approach outperforms state-of-the-art DE frameworks. Finally, we introduce a technique for improving the posterior approximation, and show that it also improves differential expression performance.

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Section: Detection Of Differentially Expressed Genesmentioning

confidence: 99%

Deep Generative Models for Detecting Differential Expression in Single Cells

Boyeau

Lopez

Regier

et al. 2019

Preprint

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“…where θ is the proportion of constant zeros of gene g in each cells, # ( ) is an indicator function which equals to 1 for = 0 and 0 for other values, +, is the pmf of the NB distribution, r is the size parameter and p is the probability parameter of the NB distribution. The zeros in expression matrix consist of constant zeros (true zeros) and zeros from NB part (dropout zeros) 20 .…”

Section: Estimating Dropout Probabilities Of Genesmentioning

confidence: 99%

scRecover: Discriminating true and false zeros in single-cell RNA-seq data for imputation

Miao

Zhang

2019

Preprint

Self Cite

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High-throughput single-cell RNA-seq (scRNA-seq) data contains excess zero values, including those of genes not expressed in the cell, and those produced due to dropout events. Existing imputation methods do not distinguish these two types of zeros. We present a modest imputation method scRecover to only impute the dropout zeros. It estimates the zero dropout probability of each gene in each cell, and predicts the number of truly expressed genes in the cell. scRecover is combined with other imputation methods like scImpute, SAVER and MAGIC to fulfil the imputation. Downsampling experiments show that it recovers dropout zeros with higher accuracy and avoids overimputing true zero values. Experiments on real data illustrate scRecover improves downstream analysis and visualization.

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“…One of the more common applications of RNA-seq data is estimating and testing for differences in gene expression between two groups. Many packages and techniques exist to perform this task [Robinson and Smyth, 2007b, Hardcastle and Kelly, 2010, Van De Wiel et al, 2012, Kharchenko et al, 2014, Law et al, 2014, Love et al, 2014, Finak et al, 2015, Guo et al, 2015, Nabavi et al, 2015, Delmans and Hemberg, 2016, Korthauer et al, 2016, Costa-Silva et al, 2017, Qiu et al, 2017, Miao et al, 2018, Van den Berge et al, 2018, Wang and Nabavi, 2018, Wang et al, 2019, and so developing approaches and software to compare these different software packages would be of great utility to the scientific community. Generating data from the two-group model is a special case of (1) and (2), where…”

Section: Application: Evaluating Differential Expression Analysismentioning

confidence: 99%

“…The zero-inflated negative binomial distribution is sometimes used to model single-cell RNAseq data as it can account for the abundance of zeros observed in such data [Miao et al, 2018, Eraslan et al, 2019. A random variable y is distributed zero-inflated negative binomial, denoted y ∼ ZINB(π, µ, φ), if it is generated by the following hierarchical process:…”

Section: S12 Generalizing the Poisson Assumptionmentioning

confidence: 99%

Data-based RNA-seq Simulations by Binomial Thinning

Gerard

2019

Preprint

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With the explosion in the number of methods designed to analyze bulk and single-cell RNAseq data, there is a growing need for approaches that assess and compare these methods. The usual technique is to compare methods on data simulated according to some theoretical model. However, as real data often exhibit violations from theoretical models, this can result in unsubstantiated claims of a method's performance. Rather than generate data from a theoretical model, in this paper we develop methods to add signal to real RNA-seq datasets. Since the resulting simulated data are not generated from an unrealistic theoretical model, they exhibit realistic (annoying) attributes of real data. This lets RNA-seq methods developers assess their procedures in non-ideal (model-violating) scenarios. Our procedures may be applied to both single-cell and bulk RNA-seq. We show that our simulation method results in more realistic datasets and can alter the conclusions of a differential expression analysis study. We also demonstrate our approach by comparing various factor analysis techniques on RNA-seq datasets. Our tools are available in the seqgendiff R package on the Comprehensive R Archive Network: https://cran.r-project.org/package=seqgendiff.

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DEsingle for detecting three types of differential expression in single-cell RNA-seq data

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 227 publications

References 43 publications

Deep Generative Models for Detecting Differential Expression in Single Cells

Deep Generative Models for Detecting Differential Expression in Single Cells

scRecover: Discriminating true and false zeros in single-cell RNA-seq data for imputation

Data-based RNA-seq Simulations by Binomial Thinning

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