Batch effects and the effective design of single-cell gene expression studies

Tung, Po-Yuan; Blischak, John; Hsiao, Chiaowen Joyce; Knowles, David A.; Burnett, Jonathan E.; Pritchard, Jonathan K.; Gilad, Yoav

doi:10.1101/062919

Cited by 55 publications

(100 citation statements)

References 54 publications

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“…Blocking on these factors may be necessary to account for batch effects that are often observed in scRNA-seq data ( Hicks et al , 2015; Tung et al , 2016). …”

Section: Analysis Of Cell Types In the Brainmentioning

confidence: 99%

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

2016

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Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

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“…Blocking on these factors may be necessary to account for batch effects that are often observed in scRNA-seq data ( Hicks et al , 2015; Tung et al , 2016). …”

Section: Analysis Of Cell Types In the Brainmentioning

confidence: 99%

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

2016

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“…We are not investigating the performance of spike-in RNA for the absolute quantification of endogenous transcript molecules (Svensson et al 2017), which would require estimation of the absolute bias in each cell. We are also not studying the use of spike-ins for batch correction (Tung et al 2017), which would require modeling of gene-specific batch effects beyond simple cellspecific scaling. Both of these tasks are separate to scaling normalization and will not be addressed here.…”

Section: Overviewmentioning

confidence: 99%

“…We performed both the premixed and separate-addition experiments on the same plate to avoid plate effects (Hicks et al 2015;Tung et al 2017). For the separate-addition experiment, we also reversed the order of addition of the two spike-in sets to determine if this affected the variance estimate.…”

Section: Description Of the Mixture Experimentsmentioning

confidence: 99%

Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data

et al. 2017

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By profiling the transcriptomes of individual cells, single-cell RNA sequencing provides unparalleled resolution to study cellular heterogeneity. However, this comes at the cost of high technical noise, including cell-specific biases in capture efficiency and library generation. One strategy for removing these biases is to add a constant amount of spike-in RNA to each cell and to scale the observed expression values so that the coverage of spike-in transcripts is constant across cells. This approach has previously been criticized as its accuracy depends on the precise addition of spike-in RNA to each sample. Here, we perform mixture experiments using two different sets of spike-in RNA to quantify the variance in the amount of spike-in RNA added to each well in a plate-based protocol. We also obtain an upper bound on the variance due to differences in behavior between the two spike-in sets. We demonstrate that both factors are small contributors to the total technical variance and have only minor effects on downstream analyses, such as detection of highly variable genes and clustering. Our results suggest that scaling normalization using spike-in transcripts is reliable enough for routine use in single-cell RNA sequencing data analyses.

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“…al 14 . Three induced pluripotent stem cell lines were sequenced in triplicate on the Fluidigm C1 platform using a total of 9 plates.…”

Section: Mucosal-associated Invariant T (Mait) Cellsmentioning

confidence: 99%

“…We applied the SCTK and our workflow on multiple data examples, including stimulated and unstimulated mucosal-associated invariant T cells, and induced pluripotent stem cells from Yoruba male reference samples to identify batch effects 14,15 . These example datasets show the SCTK's ability to identify biologically meaningful results through R console analysis using SCTK R functions, or through the GUI (Supplementary Note 1).…”

Section: Main Textmentioning

confidence: 99%

Interactive single cell RNA-Seq analysis with the Single Cell Toolkit (SCTK)

Johnson

Jenkins

Khan³

et al. 2018

Preprint

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Single cell RNA-sequencing (scRNA-Seq) allows researchers to profile transcriptional activity in individual cells. However, the complex nature of these data and variability in study design and data generation requires sophisticated computational tools and informed analytical decisions. Here, we present the Single Cell Toolkit (SCTK), an interactive scRNA-Seq analysis package that enables users to perform scRNA-Seq analysis interactively using a command-line workflow or a graphical user interface (GUI) written in R/Shiny. Main TextSingle cell RNA-sequencing (scRNA-Seq) techniques allow researchers to explore the transcriptional landscape of a sample at the resolution of the individual cell. In the context of cancer, scRNA-Seq can identify the subclonality of a tumor sample to improve our ability to identify the cell-specific mechanisms that drive tumor growth and can characterize different cellular populations within the tumor microenvironment such as immune cells 1,2 . However, different optimizations of parameters and algorithms are required for filtration, normalization, clustering, and differential expression of scRNA-Seq data compared to bulk RNA-seq due to the low amount of starting material and technical bias introduced in the common scRNA-Seq library preparation techniques 3 . Tools for normalization and analysis of scRNA-Seq data exist to overcome these technical biases, but these tools are not integrated and require command line processing of samples and knowledge of the many options available for each tool, which makes them difficult to use, especially for scientists without training in bioinformatics [4][5][6][7][8][9] . Even for more advanced users, there is still a need to interactively explore scRNA-Seq results during processing to help make dataset specific decisions that can affect downstream analysis.Here, we present the Single Cell Toolkit (SCTK), an R/Shiny 10 based package for both command line and interactive scRNA-Seq processing. Users can upload count and annotation data and interactively explore and perform analyses. Data and results can be saved in a convenient object for downstream command line analysis, or to reload into the GUI in another session. With the SCTK, it is possible to perform a full analysis workflow from uploading raw data to downloading processed results. While other tools can perform specific scRNA-Seq analysis steps, the SCTK is the first fully interactive scRNA-Seq analysis workflow available within the R language ( Table 1).. CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The SCTK is organized into several analysis modules ( Fig. 1). Analysis modules include data summary and filtering, dimensionality reduction, clustering, batch correction, differential expression, pathway activity analysis, and power calculations to evaluate the tradeoff between sample size, cell numbers, and sequencing depths. All analysis modules can be...

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Batch effects and the effective design of single-cell gene expression studies

Cited by 55 publications

References 54 publications

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data

Interactive single cell RNA-Seq analysis with the Single Cell Toolkit (SCTK)

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