powsimR: Power analysis for bulk and single cell RNA-seq experiments

Vieth, Beate; Ziegenhain, Christoph; Enard, Wolfgang; Hellmann, Ines

doi:10.1101/117150

Cited by 37 publications

(52 citation statements)

References 18 publications

(11 reference statements)

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“…MAST uses a hurdle model to account for dropout while modelling changes in gene expression dependent upon condition and technical covariates. It was the best‐performing single‐cell DE testing method in the aforementioned study (Soneson & Robinson, ), and outperformed bulk and single‐cell methods in a small‐scale comparison on a single dataset (Vieth et al , ). While MAST has a 10‐fold to 100‐fold faster runtime than weighted bulk methods (Van den Berge et al , ), a further 10‐fold speedup can be achieved using limma–voom (Law et al , ).…”

Section: Introductionmentioning

confidence: 94%

“…We cannot expect that a single normalization method is appropriate for all types of scRNA-seq data. For example, Vieth et al (2017) showed that read and count data are best fit by different models. Indeed Cole et al (2019) find that different normalization methods perform optimally for different datasets and argue that their scone tool should be used to select the appropriate normalization method for a specific dataset.…”

Section: Normalizationmentioning

confidence: 99%

“…Finally, log transformation reduces the skewness of the data to approximate the assumption of many downstream analysis tools that the data are normally distributed. While scRNA‐seq data are not in fact log‐normally distributed (Vieth et al , ), these three effects make the log transformation a crude, but useful tool. This usefulness is highlighted by downstream applications for differential expression testing (Finak et al , ; Ritchie et al , ) or batch correction (Johnson et al , ; Buttner et al , ) that use log transformation for these purposes.…”

Section: Introductionmentioning

confidence: 99%

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Current best practices in single‐cell RNA‐seq analysis: a tutorial

Luecken

Theis

2019

Molecular Systems Biology

1,534

1,386

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Single‐cell RNA ‐seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single‐cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up‐to‐date workflow to analyse one's data. Here, we detail the steps of a typical single‐cell RNA ‐seq analysis, including pre‐processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell‐ and gene‐level downstream analysis. We formulate current best‐practice recommendations for these steps based on independent comparison studies. We have integrated these best‐practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial . This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

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Section: Introductionmentioning

confidence: 94%

Section: Normalizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Current best practices in single‐cell RNA‐seq analysis: a tutorial

Luecken

Theis

2019

Molecular Systems Biology

1,534

1,386

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“…More packages are now including their simulation functions and some tools have been developed for the specific purpose of generating realistic synthetic scRNA-seq datasets (powsimR 31 , Splatter 32 ). Classification of cells into known groups has also 20, 2017; increased as reference datasets become available and more tools are identifying or making use of co-regulated gene networks.…”

Section: Figure 3 (A) Categories Of Tools In the Scrna-tools Databasementioning

confidence: 99%

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

Zappia

Phipson

Oshlack

2017

Preprint

101

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As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers.In order to better facilitate selection of appropriate analysis tools we have been cataloguing and curating new analysis tools, as they become available, in the scRNAtools database (www.scRNA-tools.org). Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools are developed to perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and as a record of the growth of the field over time.

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“…Furthermore, for typical univariate supervised inference problems such as regression, tools for power analysis exist to assess what parameter values in models can be reliably inferred from data of varying sizes [2,3]. However, questions of interest in single-cell RNA-seq analysis do not have a structure where performance can be directly quantified and appropriate subsampling and quantification of per- Figure 1) Outline of the workflow for subsampling reads and cells, fitting models with a variational autoencoder, evaluating validation error, and visualization.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq

Svensson

Beltrame

Pachter

2019

Preprint

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The allocation of a sequencing budget when designing single cell RNA-seq experiments requires consideration of the tradeoff between number of cells sequenced and the read depth per cell. One approach to the problem is to perform a power analysis for a univariate objective such as differential expression. However, many of the goals of single-cell analysis requires consideration of the multivariate structure of gene expression, such as clustering. We introduce an approach to quantifying the impact of sequencing depth and cell number on the estimation of a multivariate generative model for gene expression that is based on error analysis in the framework of a variational autoencoder. We find that at shallow depths, the marginal benefit of deeper sequencing per cell significantly outweighs the benefit of increased cell numbers. Above about 15,000 reads per cell the benefit of increased sequencing depth is minor. Code for the workflow reproducing the results of the paper is available at https://github.com/pachterlab/SBP_2019/.

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powsimR: Power analysis for bulk and single cell RNA-seq experiments

Cited by 37 publications

References 18 publications

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Current best practices in single‐cell RNA‐seq analysis: a tutorial

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

Quantifying the tradeoff between sequencing depth and cell number in single-cell RNA-seq

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