Single-Cell (Multi)omics Technologies

Chappell, Lia; Russell, Andrew J.; Voet, Thierry

doi:10.1146/annurev-genom-091416-035324

Cited by 175 publications

(149 citation statements)

References 164 publications

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“…As single-cell technologies mature, they are applied to generate data sets of increasing complexity, with highly structured and sparse measurements 16,17,24,48,49 . Consequently, there is a need for integrative computational frameworks that can robustly and systematically interrogate the data generated in order to reveal the underlying sources of variation 25 .…”

Section: Discussionmentioning

confidence: 99%

MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data

Argelaguet

Arnol

Bredikhin

et al. 2019

Preprint

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Technological advances have enabled the joint analysis of multiple molecular layers at single cell resolution. At the same time, increased experimental throughput has facilitated the study of larger numbers of experimental conditions. While methods for analysing single-cell data that model the resulting structure of either of these dimensions are beginning to emerge, current methods do not account for complex experimental designs that include both multiple views (modalities or assays) and groups (conditions or experiments). Here we present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of structured single cell multi-modal data. MOFA+ builds upon a Bayesian Factor Analysis framework combined with fast GPU-accelerated stochastic variational inference. Similar to existing factor models, MOFA+ allows for interpreting variation in single-cell datasets by pooling information across cells and features to reconstruct a low-dimensional representation of the data. Uniquely, the model supports flexible group-level sparsity constraints that allow joint modelling of variation across multiple groups and views.To illustrate MOFA+, we applied it to single-cell data sets of different scales and designs, demonstrating practical advantages when analyzing datasets with complex group and/or view structure. In a multi-omics analysis of mouse gastrulation this joint modelling reveals coordinated changes between gene expression and epigenetic variation associated with cell fate commitment.

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Section: Discussionmentioning

confidence: 99%

MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data

Argelaguet

Arnol

Bredikhin

et al. 2019

Preprint

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“…This enables the correlation of the expression of different RNA species with each other and with cellular phenotypes. Additionally, we and others have recently shown how RNA smFISH data can lead to better more predictive models of gene expression due to: 1) the fact that they contain information on more gene regulatory processes, including transcription (initiation, elongation), RNA export, and RNA degradation, and 2) they enable direct measurement of the distribution of RNA molecules which are essential for single cell modeling 20-27 .…”

Section: Background and Summarymentioning

confidence: 99%

“…Because this data set contains both 3D spatial and temporal information on the expression of these RNAs, it can be used to simultaneously investigate many different important processes regulating transcription levels as they occur in the cell. Such analyses are not possible in cell population-based or single cell sequencing experiments 1-5,27 . Here we present RNA expression as population mean, fraction of cells above basal mRNA expression (ON-cells), the variance normalized by the expression mean (Fano factor) (Figure 1), marginal probability of nuclear and cytoplasmic mRNA (Figure 2), and the joint probability of nuclear and cytoplasmic RNA expression (Figure 3).…”

Section: Background and Summarymentioning

confidence: 99%

Multiplex RNA single molecule FISH of inducible mRNAs in single yeast cells

Neuert

2019

Preprint

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Transcript levels powerfully influence cell behavior and phenotype and are carefully regulated at several steps. Recently developed single cell approaches such as RNA single molecule fluorescence in-situ hybridization (smFISH) have produced advances in our understanding of how these steps work within the cell. In comparison to single-cell sequencing, smFISH provides more accurate quantification of RNA levels. Additionally, transcript subcellular localization is directly visualized, enabling the analysis of transcription (initiation and elongation), RNA export and degradation. As part of our efforts to investigate how this type of analysis can generate improved models of gene expression, we used smFISH to quantify the kinetic expression of STL1 and CTT1 mRNAs in single Saccharomyces cerevisiae cells upon 0.2 and 0.4M NaCl osmotic stress. In this Data Descriptor, we outline our procedure along with our data in the form of raw images and processed mRNA counts. We discuss how these data can be used to develop single cell modelling approaches, to study fundamental processes in transcription regulation and develop single cell image processing approaches.

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“…Comprehensive atlases of gene expression are being built for tissues such as the Drosophila brain throughout its lifespan 4 to an entire mouse 5 . Inspired by the wealth of new insights from single-cell RNA-seq, there has been a plethora of single cell genomic technologies developed in the last few year (reviewed in 6 ) . For example, single-cell profiling of chromatin accessibility [7][8][9] has generated a lot of excitement because of the wealth of insights generated within large scale surveys of chromatin accessibility and gene regulation through projects like ENCODE 10 .…”

Section: Introductionmentioning

confidence: 99%

Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self-Organizing Maps

Jansen

Ramirez

El-Ali

et al. 2018

Preprint

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Rapid advances in single-cell assays have outpaced methods for analysis of those data types.Different single-cell assays show extensive variation in sensitivity and signal to noise levels. In particular, scATAC-seq generates extremely sparse and noisy datasets. Existing methods developed to analyze this data require cells amenable to pseudo-time analysis or require datasets with drastically different cell-types. We describe a novel approach using self-organizing maps (SOM) to link scATAC-seq and scRNA-seq data that overcomes these challenges and can generate draft regulatory networks. Our SOMatic package generates chromatin and gene expression SOMs separately and combines them using a linking function. We applied SOMatic on a mouse pre-B cell differentiation time-course using controlled Ikaros over-expression to recover gene ontology enrichments, identify motifs in genomic regions showing similar singlecell profiles, and generate a gene regulatory network that both recovers known interactions and predicts new Ikaros targets during the differentiation process. The ability of linked SOMs to detect emergent properties from multiple types of highly-dimensional genomic data with very different signal properties opens new avenues for integrative analysis of single-cells.

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Single-Cell (Multi)omics Technologies

Cited by 175 publications

References 164 publications

MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data

MOFA+: a probabilistic framework for comprehensive integration of structured single-cell data

Multiplex RNA single molecule FISH of inducible mRNAs in single yeast cells

Building gene regulatory networks from scATAC-seq and scRNA-seq using Linked Self-Organizing Maps

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