Computational methods for single-cell omics across modalities

Efremova, Mirjana; Teichmann, Sarah A.

doi:10.1038/s41592-019-0692-4

Cited by 179 publications

(152 citation statements)

References 32 publications

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“…Since the first step of scLink was partially motivated by our scImpute method [29] and additional imputation methods for single-cell gene expression data have also become available [83], an alternative approach to the construction of gene co-expression networks is to apply conventional inference methods on imputed gene expression data. However, we would like to discuss two potential issues with this approach.…”

Section: Discussionmentioning

confidence: 99%

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

2020

Preprint

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A system-level understanding of the regulation and coordination mechanisms of gene expression is essential to understanding the complexity of biological processes in health and disease. With the rapid development of single-cell RNA sequencing technologies, it is now possible to investigate gene interactions in a cell-type-specific manner. Here we propose the scLink method, which uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data. We use both simulation and real data studies to demonstrate the advantages of scLink and its ability to improve single-cell gene network analysis. The source code used in this article is available at https://github.com/Vivianstats/scLink.

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Section: Discussionmentioning

confidence: 99%

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

2020

Preprint

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“…With the increase in experimental protocols that generate large amounts of data (Efremova & Teichmann, ) (such as sequencing, microscopy) and the accumulation of large data repositories (e.g., of medical records), we expect DGMs to find numerous new applications and challenges. For example, the case of clinical trial data is particularly sensitive because of privacy issues: It may be possible to identify the participants from inspection of their data.…”

Section: Perspectivesmentioning

confidence: 99%

Enhancing scientific discoveries in molecular biology with deep generative models

Lopez

Gayoso

Yosef

2020

Molecular Systems Biology

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Generative models provide a well‐established statistical framework for evaluating uncertainty and deriving conclusions from large data sets especially in the presence of noise, sparsity, and bias. Initially developed for computer vision and natural language processing, these models have been shown to effectively summarize the complexity that underlies many types of data and enable a range of applications including supervised learning tasks, such as assigning labels to images; unsupervised learning tasks, such as dimensionality reduction; and out‐of‐sample generation, such as de novo image synthesis. With this early success, the power of generative models is now being increasingly leveraged in molecular biology, with applications ranging from designing new molecules with properties of interest to identifying deleterious mutations in our genomes and to dissecting transcriptional variability between single cells. In this review, we provide a brief overview of the technical notions behind generative models and their implementation with deep learning techniques. We then describe several different ways in which these models can be utilized in practice, using several recent applications in molecular biology as examples.

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“…While these findings improve our present understanding of cellular determinants of ageing with regard to motility patterns, and its utility to serve as robust biomarkers of ageing, it remains unclear whether and how cells transition across motility states as a function of increasing age. This diversity in cellular phenotypes with age is likely linked to underlying molecular programs, cellular subtypes and cell cycle states that together influence the motility patterns of cells 5,33 . We anticipate that future work is needed to address this, which will require the use of cells derived from large cohorts of healthy donors (cross-sectional and longitudinal) imaged for long periods of time (order of days), coupled with single-cell molecular assessments.…”

Section: Singlementioning

confidence: 99%

Fractional re-distribution among cell motility states during ageing

Phillip

Zamponi

Phillip

et al. 2020

Preprint

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Ageing in humans is associated with a decreased capacity to regulate cell physiology. Cellular properties, such as cell morphology and cell mechanics, encode ageing information and as a result can be used as robust ageing biomarkers. Using a panel of dermal fibroblasts derived from healthy donors spanning a wide age range, we observe an age-related reduction in average cell motility, which we show is not due to the decreased motility of all cells, but results from fractional redistribution among motility states. By taking advantage of the single-cell nature of our motility data, we show that cells can be classified based on spatial and activity patterns that define age-dependent motility states. These findings highlight an important feature of ageing cells shown by the decrease in the heterogeneity of cell movement in older adults, that potentially offer new mechanistic insights into the ageing process and avenues for novel biomarker development.

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Computational methods for single-cell omics across modalities

Cited by 179 publications

References 32 publications

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data

Enhancing scientific discoveries in molecular biology with deep generative models

Fractional re-distribution among cell motility states during ageing

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