Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies

Fusi, Nicoló; Stegle, Oliver; Lawrence, Neil D.

doi:10.1371/journal.pcbi.1002330

Cited by 106 publications

(148 citation statements)

References 27 publications

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“…This method (Fig. 1) builds on existing approaches for modeling gene expression heterogeneity in bulk data 22,30 , which we here adapt to single-cell transcriptomics. We have validated our method using a large mESC data set in which the cellcycle stages of individual cells are known a priori (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…This approach can also be used to create a 'corrected' gene expression data set, in which the effect of the identified factor(s) is removed, which can be used as the input for existing analysis methods. scLVM is related to approaches for modeling variability in bulk mRNA expression studies 21,22 and to methods used in genome-wide association studies in which the relatedness between individuals is inferred from genotype 29 and/or expression levels 30 and then accounted for in downstream analyses using linear mixed models.…”

Section: Development Of Sclvm To Account For Effects Of the Cell Cyclementioning

confidence: 99%

“…Additionally, the hidden factors can be accounted for when performing pairwise gene-gene correlation analyses, and further allow 'corrected' residual gene expression data sets to be generated. scLVM is closely related to previous approaches that correct for hidden confounding factors in gene expression data 21,48 and the inference employed to fit hidden factors builds on the PANAMA model 30 .…”

Section: Supplementary Data 1)mentioning

confidence: 99%

“…However, instead of explicitly reconstructing PCA loadings and scores, the GPLVM approach fits a low-rank cell-to-cell covariance to the observed gene expression matrix of these genes. Related approaches have been proposed to account for relatedness between individuals in the context of expression Quantitative Trait Loci (eQTL) studies 30 , where an individual-to-individual covariance is inferred to explain the heterogeneity in gene expression levels between individuals rather than cells.…”

Section: Supplementary Data 1)mentioning

confidence: 99%

“…22 and Choosing the rank of the cell-cycle factor). In general, the P-value distribution of a test statistic on the residual data set 48 , heuristic selection approaches 21 or hierarchical modeling to regularize the effective dimensionality of the hidden factor 22,30 can also be employed (Supplementary Notes).…”

Section: Supplementary Data 1)mentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%