Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression

Kim, Jong; Kolodziejczyk, Aleksandra A.; Ilicic, Tomislav; Teichmann, Sarah A.; Marioni, John C.

doi:10.1038/ncomms9687

Cited by 235 publications

(229 citation statements)

References 28 publications

(49 reference statements)

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“…The problem of low sensitivity is amplified by the low capture rate of transcripts in single cell–gene expression analysis (typically 60% to 90% of transcripts of a given cell are lost) and by technical (sampling) noise [25–27]. This can lead to false-positive sets of mutually exclusive expression of transcripts and thereby inflate cell-cell diversity, which is a crucial quantity in our analysis (S1 Appendix, A.8) [28]. …”

Section: Resultsmentioning

confidence: 99%

Cell Fate Decision as High-Dimensional Critical State Transition

et al. 2016

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Cell fate choice and commitment of multipotent progenitor cells to a differentiated lineage requires broad changes of their gene expression profile. But how progenitor cells overcome the stability of their gene expression configuration (attractor) to exit the attractor in one direction remains elusive. Here we show that commitment of blood progenitor cells to the erythroid or myeloid lineage is preceded by the destabilization of their high-dimensional attractor state, such that differentiating cells undergo a critical state transition. Single-cell resolution analysis of gene expression in populations of differentiating cells affords a new quantitative index for predicting critical transitions in a high-dimensional state space based on decrease of correlation between cells and concomitant increase of correlation between genes as cells approach a tipping point. The detection of “rebellious cells” that enter the fate opposite to the one intended corroborates the model of preceding destabilization of a progenitor attractor. Thus, early warning signals associated with critical transitions can be detected in statistical ensembles of high-dimensional systems, offering a formal theory-based approach for analyzing single-cell molecular profiles that goes beyond current computational pattern recognition, does not require knowledge of specific pathways, and could be used to predict impending major shifts in development and disease.

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Section: Resultsmentioning

confidence: 99%

Cell Fate Decision as High-Dimensional Critical State Transition

et al. 2016

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“…There are many other strategies for defining HVGs, e.g., by using the coefficient of variation ( Brennecke et al , 2013; Kim et al , 2015; Kolodziejczyk et al , 2015), with the dispersion parameter in the negative binomial distribution ( McCarthy et al , 2012), or as a proportion of total variability ( Vallejos et al , 2015). Some of these methods are available in scran – for example, see or for calculations based on the coefficient of variation.…”

Section: Analysis Of Haematopoietic Stem Cellsmentioning

confidence: 99%

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

2016

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Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

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“…Previously, our lab and others demonstrated that single-cell RNA-seq facilitates the identification of ASE patterns. [21][22][23][24] In this study, we sequenced the RNA of 1,084 single fibroblasts from 5 individuals. For 2 of these individuals, the parental DNA was available.…”

Section: Introductionmentioning

confidence: 99%

Detection of Imprinted Genes by Single-Cell Allele-Specific Gene Expression

Santoni

Stamoulis

Garieri

et al. 2017

The American Journal of Human Genetics

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Genomic imprinting results in parental-specific gene expression. Imprinted genes are involved in the etiology of rare syndromes and have been associated with common diseases such as diabetes and cancer. Standard RNA bulk cell sequencing applied to whole-tissue samples has been used to detect imprinted genes in human and mouse models. However, lowly expressed genes cannot be detected by using RNA bulk approaches. Here, we report an original and robust method that combines single-cell RNA-seq and whole-genome sequencing into an optimized statistical framework to analyze genomic imprinting in specific cell types and in different individuals. Using samples from the probands of 2 family trios and 3 unrelated individuals, 1,084 individual primary fibroblasts were RNA sequenced and more than 700,000 informative heterozygous single-nucleotide variations (SNVs) were genotyped. The allele-specific coverage per gene of each SNV in each single cell was used to fit a beta-binomial distribution to model the likelihood of a gene being expressed from one and the same allele. Genes presenting a significant aggregate allelic ratio (between 0.9 and 1) were retained to identify of the allelic parent of origin. Our approach allowed us to validate the imprinting status of all of the known imprinted genes expressed in fibroblasts and the discovery of nine putative imprinted genes, thereby demonstrating the advantages of single-cell over bulk RNA-seq to identify imprinted genes. The proposed single-cell methodology is a powerful tool for establishing a cell type-specific map of genomic imprinting.

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Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression

Cited by 235 publications

References 28 publications

Cell Fate Decision as High-Dimensional Critical State Transition

Cell Fate Decision as High-Dimensional Critical State Transition

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

Detection of Imprinted Genes by Single-Cell Allele-Specific Gene Expression

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