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

By Rhonda L. Bacher, Christina Kendziorski. Genome Biology, 2016

433 citations recorded by the source corpus.

Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allows us to address scientific questions that eluded examination just a few years ago. With the advantages of scRNA-seq come computational challenges that are just beginning to be addressed. In this article, we highlight the computational methods available for the design and analysis of scRNA-seq experiments, their advantages and disadvantages in various settings, the open questions for which novel methods are needed, and expected future developments in this exciting area.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5de665f5bbf2988bb66787dca9da5fb502d7c112

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Citations in the corpus

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics (2017, background): “We describe a few popular approaches here; for a thorough review see [11, 12].”
Droplet scRNA-seq is not zero-inflated (2019, background): “That is, for any given gene many cells had not detected the expression, even if the level was relatively high in other cells (Bacher and Kendziorski, 2016; Haque et al., 2017; Vallejos et al., 2017).”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “If spike-ins or UMIs are used in scRNA-seq protocol, normalization can be reﬁned based on the performance of spike-ins/UMIs (Bacher and Kendziorski, 2016).”
A general and flexible method for signal extraction from single-cell RNA-seq data (2017, neutral): “Despite the early successes of scRNA-seq, in order to fully exploit the potential of this new technology, it is essential to develop statistical and computational methods specifically designed for the unique challenges of this type of data [7].”
Modelling dropouts for feature selection in scRNASeq experiments (2016, neutral): “A typical scRNA-seq experiment has ~50% dropouts (Table 1) [22] the frequency of zero values (dropout rate) relative to the mean expression of the gene could equally be used.”
PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data (2019, background): “Compared with bulk RNA-seq, scRNA-seq data contain many zero measurements, caused by dropout events and a cleaner biological signal (3).”
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, methodology): “…and impact of different sources of noise are poorly understood, and numerous factors contribute to the biological heterogeneity observed within cell populations (Stegle et al. 2015, Pierson et al. 2015, Grün et al. 2015, Bacher & Kendziorski 2016, Liu et al. 2016, Buettner et al. 2015).”
DrImpute: imputing dropout events in single cell RNA sequencing data (2017, background): “80 81 However, scRNA-seq has a relatively higher noise level than bulk RNA-seq 82 especially due to so-called dropout events ([2], [23], [24]).”
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications (2017, methodology): “This has been augmented by a number of methods involving different machine-learning algorithms, including for example t-distributed stochastic neighbour embedding (t-SNE) and Gaussian process latent variable modelling (GPLVM), which have been reviewed in detail elsewhere [74, 82, 83].”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, methodology): “Specific tools have been created for analysis of single-cell RNAseq data and are reviewed in [57].”