Detecting and correcting systematic variation in large-scale RNA sequencing data

Li, Sheng; Łabaj, Paweł P.; Zumbo, Paul; Sykacek, Peter; Shi, Wei; Shi, Leming; Phan, John H.; Wu, Po-Yen; Wang, May D.; Wang, Charles; Thierry-Mieg, Danielle; Thierry‐Mieg, Jean; Kreil, David P.; Mason, Christopher E.

doi:10.1038/nbt.3000

Cited by 172 publications

(145 citation statements)

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“…To fully exploit single-cell RNA-seq data, we have to account for the random noise inherent to such data sets 20 and, equally important, to account for different hidden factors that might result in gene expression heterogeneity. Although the importance of accounting for unobserved factors is well established in bulk RNA-seq studies [21][22][23] , robust approaches to detect and account for confounding factors in single-cell RNA-seq studies remain to be developed. Here, we describe a computational approach that uses latent variable models to reconstruct such hidden factors from the observed data.…”

Section: A N a Ly S I Smentioning

confidence: 99%

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

et al. 2015

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Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.Single-cell measurements of gene expression, using imaging techniques such as RNA-FiSH (fluorescence in situ hybridization), have provided important insights into the kinetics of transcription and cell-to-cell variation in gene expression [1][2][3] . However, such approaches can examine the expression of only a small number of genes in each experiment, thus restricting our ability to examine co-expression patterns and to robustly identify subpopulations of cells. Protocols have been developed to overcome these limitations by amplifying small quantities of mRNA 4,5 , which, in combination with microfluidics approaches for isolating individual cells 6,7 , have been used to analyze the co-expression of tens to hundreds of genes in single cells 8,9 . These protocols also allow the entire transcriptome of large numbers of single cells to be assayed in an unbiased way. This was initially done using microarrays 10,11 but is more often now done using next-generation sequencing [12][13][14][15] . Such approaches have been used to model early embryogenesis in the mouse 16 and to investigate bimodality in gene expression patterns of differentiating immune cell types 17 .After the generation of single-cell RNA-sequencing (RNA-seq) profiles from hundreds of cells, one goal to identify subpopulations that share a common gene-expression profile. Some of these subpopulations may represent previously unidentified cell types. Additionally, by studying patterns of gene expression in different single cells, insights into the regulatory landscape of each cell population can be obtained.However, methods for identifying subpopulations of cells and modeling their gene regulatory landscapes are only now beginning to emerge 18,19 . To fully exploit single-cell RNA-seq data, we have to account for the random noise inherent to such data sets 20 and, equally important, to account for different hidden factors that might result in gene expression heterogeneity. Although the importance of accounting for unobserved factors is well established in bulk RNA-seq studies [21][22][23] , robust approaches to detect and account for confounding f...

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Section: A N a Ly S I Smentioning

confidence: 99%

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

et al. 2015

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“…In fact, the fraction of variation across laboratories correlates with the GC content of each gene (Fig. 3c), and recapitulates the known role of GC content with reproducibility of RNA-seq data [8, [41][42][43].…”

Section: Analysis Of Seqc Rna-seq Datasetmentioning

confidence: 99%

variancePartition: Interpreting drivers of variation in complex gene expression studies

Hoffman

Schadt

2016

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118

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Background: As large-scale studies of gene expression with multiple sources of biological and technical variation become widely adopted, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. Results: We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation based on a genome-wide summary, and identify genes that deviate from the genome-wide trend. Using a linear mixed model, variancePartition quantifies variation in each expression trait attributable to differences in disease status, sex, cell or tissue type, ancestry, genetic background, experimental stimulus, or technical variables. Analysis of four large-scale transcriptome profiling datasets illustrates that variancePartition recovers striking patterns of biological and technical variation that are reproducible across multiple datasets. Conclusions:Our open source software, variancePartition, enables rapid interpretation of complex gene expression studies as well as other high-throughput genomics assays. variancePartition is available from Bioconductor: http://bioconductor.org/packages/variancePartition.

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“…Both RNA-Seq and microarrays are affected by systematic variations (Park et al, 2003;Oshlack and Wakefield, 2009;Zheng et al, 2011;Li et al, 2014b). Therefore, genomewide expression results generated by either technique need to be normalized before analysis (Dillies et al, 2013;Li et al, 2015b).…”

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Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

et al. 2017

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ORCID IDs: 0000-0002-6182-800X (J.H.); 0000-0001-6612-3570 (S.V.); 0000-0002-9564-8146 (K.M.M.).With the emergence of massively parallel sequencing, genomewide expression data production has reached an unprecedented level. This abundance of data has greatly facilitated maize research, but may not be amenable to traditional analysis techniques that were optimized for other data types. Using publicly available data, a gene coexpression network (GCN) can be constructed and used for gene function prediction, candidate gene selection, and improving understanding of regulatory pathways. Several GCN studies have been done in maize (Zea mays), mostly using microarray datasets. To build an optimal GCN from plant materials RNA-Seq data, parameters for expression data normalization and network inference were evaluated. A comprehensive evaluation of these two parameters and a ranked aggregation strategy on network performance, using libraries from 1266 maize samples, were conducted. Three normalization methods and 10 inference methods, including six correlation and four mutual information methods, were tested. The three normalization methods had very similar performance. For network inference, correlation methods performed better than mutual information methods at some genes. Increasing sample size also had a positive effect on GCN. Aggregating single networks together resulted in improved performance compared to single networks.Maize (Zea mays) is the most widely produced crop in United States, and U.S. agriculture accounted for 36% of world maize production in 2015 (USDA, 2016). Maize has also been in the center of genetics research for more than 100 years, including McClintock's pioneering work with transposable elements (reviewed by McClintock, 1983;Fedoroff, 2012). Due to recent technological advances in nucleic acid sequencing and the availability of the maize genome sequence (Schnable et al., 2009), maize genomics research has been greatly expedited.RNA-sequencing (RNA-Seq) has become the favored technique for detecting genomewide expression patterns. RNA-Seq has some advantages over microarray analysis of gene expression, including single base-pair resolution, detection of novel transcripts, and the ability to analyze transcript abundance without existing genome information (reviewed by Wang et al., 2009;Han et al., 2015;Conesa et al., 2016). RNA-Seq data provides information about single nucleotide polymorphisms, which facilitates genomewide association studies (Fu et al., 2013;Li et al., 2013a;Lonsdale et al., 2013;Fadista et al., 2014). Because of its widespread adaptability, greater than 5000 Illumina platform Maize RNA-Seq libraries (Fig. 1A) are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database (Leinonen et al., 2010), adding to the body of data that can be used to study the maize genome.The maize genome is large and heterogeneous, and the genome annotation is still far from complete (Cigan et al., 2005;Ficklin and Feltus, 2011). Although recent work has...

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Detecting and correcting systematic variation in large-scale RNA sequencing data

Cited by 172 publications

References 59 publications

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

variancePartition: Interpreting drivers of variation in complex gene expression studies

Construction and Optimization of a Large Gene Coexpression Network in Maize Using RNA-Seq Data

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