MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Finak, Greg; McDavid, Andrew; Yajima, Masanao; Deng, Jingyuan; Gersuk, Vivian H.; Shalek, Alex K.; Slichter, Chloe K.; Miller, Hannah W.; McElrath, M. Juliana; Prlic, Martin; Linsley, Peter S.; Gottardo, Raphaël

doi:10.1186/s13059-015-0844-5

Cited by 2,527 publications

(2,478 citation statements)

References 34 publications

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“…6), we used a two part ‘hurdle’ model to control for both technical quality and mouse-to-mouse variation. This was implemented using the R package MAST 59 , and p -values for differential expression were computed using the likelihood-ratio test. Multiple hypothesis testing correction was performed by controlling the false discovery rate 60 using the R function p.adjust.…”

Section: Methodsmentioning

confidence: 99%

A single-cell survey of the small intestinal epithelium

Haber

Biton

Rogel

et al. 2017

Nature

1,416

115

1,694

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Intestinal epithelial cells (IECs) absorb nutrients, respond to microbes, provide barrier function and help coordinate immune responses. We profiled 53,193 individual epithelial cells from mouse small intestine and organoids, and characterized novel subtypes and their gene signatures. We showed unexpected diversity of hormone-secreting enteroendocrine cells and constructed their novel taxonomy. We distinguished between two tuft cell subtypes, one of which expresses the epithelial cytokine TSLP and CD45 (Ptprc), the pan-immune marker not previously associated with non-hematopoietic cells. We also characterized how cell-intrinsic states and cell proportions respond to bacterial and helminth infections. Salmonella infection caused an increase in Paneth cells and enterocytes abundance, and broad activation of an antimicrobial program. In contrast, Heligmosomoides polygyrus caused an expansion of goblet and tuft cell populations. Our survey highlights new markers and programs, associates sensory molecules to cell types, and uncovers principles of gut homeostasis and response to pathogens.

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Section: Methodsmentioning

confidence: 99%

A single-cell survey of the small intestinal epithelium

Haber

Biton

Rogel

et al. 2017

Nature

1,416

115

1,694

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“…To demonstrate how interactive analysis can be performed in the SCTK, we will use an example dataset of mucosal-associated invariant T (MAIT) cells 15 . A set of 96 CD8+ MAIT cells were sorted, 47 cells were stimulated with cytokines, and the cells were processed and sequenced using the Fluidigm C1 system.…”

Section: Mucosal-associated Invariant T (Mait) Cellsmentioning

confidence: 99%

“…MAST, Model-based Analysis of Single-cell Transcriptomics, has been developed to address these issues by using a hurdle model 15 . MAST has been implemented within the SCTK.…”

Section: Differential Expression and Biomarker Creationmentioning

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).…”

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confidence: 99%

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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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“…Clusters can then be annotated based on domain-specific knowledge of the expression of a few genes, or automatically based on gene set enrichment. Finally, specific genes that are differentially expressed between clusters can be identified using scRNA-Seq-specific methods such as SCDE (Kharchenko et al, 2014) and MAST (Finak et al, 2015). (B) Most pseudotime analyses (which place each cell on a statistically derived axis that represents progression along a process, such as developmental time) start by performing dimension reduction.…”

Section: The Basics Of Scrna-seq Analysismentioning

confidence: 99%

Understanding development and stem cells using single cell-based analyses of gene expression

2017

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In recent years, genome-wide profiling approaches have begun to uncover the molecular programs that drive developmental processes. In particular, technical advances that enable genome-wide profiling of thousands of individual cells have provided the tantalizing prospect of cataloging cell type diversity and developmental dynamics in a quantitative and comprehensive manner. Here, we review how singlecell RNA sequencing has provided key insights into mammalian developmental and stem cell biology, emphasizing the analytical approaches that are specific to studying gene expression in single cells.

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MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Cited by 2,527 publications

References 34 publications

A single-cell survey of the small intestinal epithelium

A single-cell survey of the small intestinal epithelium

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

Understanding development and stem cells using single cell-based analyses of gene expression

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