Design and computational analysis of single-cell RNA-sequencing experiments

Bacher, Rhonda; Kendziorski, Christina

doi:10.1186/s13059-016-0927-y

Cited by 455 publications

(375 citation statements)

References 94 publications

(166 reference statements)

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“…These methods attempt to order cells into smooth continuous spatiotemporal trajectories to model development. However, Destiny lacks unsupervised statistics; Wanderlust typically is perfomed on few genes (<50); and Monocle, Diffusion Pseudotime, Wishbone, and SCUBA are biased (Bacher and Kendziorski 2016;Rizvi et al 2017) or depend on a few well-known markers to define the bifurcation. Based on topological data analysis (TDA), recently published scTDA (Rizvi et al 2017) has overcome some of the limitations.…”

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confidence: 99%

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

et al. 2017

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The stochastic dynamics and regulatory mechanisms that govern differentiation of individual human neural precursor cells (NPC) into mature neurons are currently not fully understood. Here, we used single-cell RNA-sequencing (scRNA-seq) of developing neurons to dissect/identify NPC subtypes and critical developmental stages of alternative lineage specifications. This study comprises an unsupervised, high-resolution strategy for identifying cell developmental bifurcations, tracking the stochastic transcript kinetics of the subpopulations, elucidating regulatory networks, and finding key regulators. Our data revealed the bifurcation and developmental tracks of the two NPC subpopulations, and we captured an early (24 h) transition phase that leads to alternative neuronal specifications. The consequent up-regulation and down-regulation of stageand subpopulation-specific gene groups during the course of maturation revealed biological insights with regard to key regulatory transcription factors and lincRNAs that control cellular programs in the identified neuronal subpopulations.

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confidence: 99%

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

et al. 2017

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“…As has been described in previous reviews a standard scRNA-seq analysis consists of several tasks which can be completed using various tools 6 . In the scRNA-tools database we categorise tools based on the analysis tasks they perform.…”

Section: Categories Of Scrna-seq Analysismentioning

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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“…2). We refer the reader to other reviews that discuss the many pre-processing and quality-control steps that are required to produce 'clean', informative single-cell data (Bacher and Kendziorski, 2016;Stegle et al, 2015), and that describe methods to detect and account for uninteresting confounding effects, such as the stage of cell cycle Vallejos et al, 2015), and to analyze and account for technical noise and the so-called 'drop out' (see Glossary, Box 1) effect (Brennecke et al, 2013;Grün et al, 2014;Kharchenko et al, 2014;Pierson and Yau, 2015).…”

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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Design and computational analysis of single-cell RNA-sequencing experiments

Cited by 455 publications

References 94 publications

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons

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

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

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