Comparison of methods to detect differentially expressed genes between single-cell populations

Jaakkola, Maria K.; Seyednasrollah, Fatemeh; Mehmood, Arfa; Elo, Laura L.

doi:10.1093/bib/bbw057

Cited by 93 publications

(123 citation statements)

References 21 publications

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“…Similarly to what was previously done by others (Kharchenko et al, 2014; Jaakkola et al, 2016), we used the top 1,000 DEGs from Moliner et al as “positive control” to test the ability of the benchmarked tools to detect true positive genes. ScRNA-seq data, containing raw counts for 22,928 genes (excluded 8 spike-ins), were retrieved from GEO database with accession number GSE29087.…”

Section: Methodsmentioning

confidence: 99%

Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods

2017

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The sequencing of the transcriptomes of single-cells, or single-cell RNA-sequencing, has now become the dominant technology for the identification of novel cell types and for the study of stochastic gene expression. In recent years, various tools for analyzing single-cell RNA-sequencing data have been proposed, many of them with the purpose of performing differentially expression analysis. In this work, we compare four different tools for single-cell RNA-sequencing differential expression, together with two popular methods originally developed for the analysis of bulk RNA-sequencing data, but largely applied to single-cell data. We discuss results obtained on two real and one synthetic dataset, along with considerations about the perspectives of single-cell differential expression analysis. In particular, we explore the methods performance in four different scenarios, mimicking different unimodal or bimodal distributions of the data, as characteristic of single-cell transcriptomics. We observed marked differences between the selected methods in terms of precision and recall, the number of detected differentially expressed genes and the overall performance. Globally, the results obtained in our study suggest that is difficult to identify a best performing tool and that efforts are needed to improve the methodologies for single-cell RNA-sequencing data analysis and gain better accuracy of results.

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Section: Methodsmentioning

confidence: 99%

Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods

2017

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“…2H). The slowest tool was SCDE (684 min), as reported in previous studies (Sengupta et al 2016;Jaakkola et al 2017). We next compared the scalability of bigSCale to MAST with respect to samples sizes.…”

Section: Identification Of Differentially Expressed Genesmentioning

confidence: 98%

bigSCale: an analytical framework for big-scale single-cell data

et al. 2018

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Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin (Reln)-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets.[Supplemental material is available for this article.]Single-cell RNA sequencing (scRNA-seq) is at the forefront of techniques to chart molecular properties of individual cells. Recent microfluidic-based methods are scalable to tens of thousands of cells, enabling an unbiased sampling and in-depth characterization without prior knowledge Macosko et al. 2015;Zheng et al. 2017). Consequently, studies are less confined by the number of cells and aim to produce comprehensive cellular atlases of entire tissues, organs, and organisms (Regev et al. 2017). Increasing cell numbers, however, generate extremely large data sets, which extend processing time and challenge computing resources. Current scRNA-seq analysis tools are not designed to analyze data sets larger than thousands of cells and often lack sensitivity and specificity to identify marker genes for cell populations or experimental conditions.To address the challenges of large scRNA-seq data sets, we developed bigSCale, an analytical framework for the sensitive detection of population markers and differentially expressed genes, being scalable to analyze mil...

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“…Similarly as in our recent study [9], we compared different cell populations. The count table was downloaded from GEO with accession number GSE70580.…”

Section: Resultsmentioning

confidence: 99%

ROTS: An R package for reproducibility-optimized statistical testing

et al. 2017

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Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).

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Comparison of methods to detect differentially expressed genes between single-cell populations

Cited by 93 publications

References 21 publications

Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods

Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods

bigSCale: an analytical framework for big-scale single-cell data

ROTS: An R package for reproducibility-optimized statistical testing

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