scMET: Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

AbstractHigh throughput measurements of DNA methylomes at single-cell resolution are a promising resource to quantify the heterogeneity of DNA methylation and uncover its role in gene regulation. However, limitations of the technology result in sparse CpG coverage, effectively posing challenges to robustly quantify genuine DNA methylation heterogeneity. Here we tackle these issues by introducing scMET, a hierarchical Bayesian model which overcomes data sparsity by sharing information across cells and genomic features, resulting in a robust and biologically interpretable quantification of variability. scMET can be used to both identify highly variable features that drive epigenetic heterogeneity and perform differential methylation and differential variability analysis between pre-specified groups of cells. We demonstrate scMET’s effectiveness on some recent large scale single cell methylation datasets, showing that the scMET feature selection approach facilitates the characterisation of epigenetically distinct cell populations. Moreover, we illustrate how scMET variability estimates enable the formulation of novel biological hypotheses on the epigenetic regulation of gene expression in early development. An R package implementation of scMET is publicly available at https://github.com/andreaskapou/scMET.

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scMET: Bayesian modeling of DNA methylation heterogeneity at single-cell resolution

Genome Biology ◽

10.1186/s13059-021-02329-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Chantriolnt-Andreas Kapourani ◽

Ricard Argelaguet ◽

Guido Sanguinetti ◽

Catalina A. Vallejos

Keyword(s):

Single Cell ◽

Bayesian Modeling ◽

Bayesian Model ◽

Regulation Of Gene Expression ◽

Differential Methylation ◽

Hierarchical Bayesian Model ◽

Genomic Features ◽

Distinct Cell ◽

Biological Heterogeneity

AbstractHigh-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. scMET is available at https://github.com/andreaskapou/scMET.

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EpiScanpy: integrated single-cell epigenomic analysis

10.1101/648097 ◽

2019 ◽

Cited By ~ 4

Author(s):

Anna Danese ◽

Maria L. Richter ◽

David S. Fischer ◽

Fabian J. Theis ◽

Maria Colomé-Tatché

Keyword(s):

Dna Methylation ◽

Single Cell ◽

Large Scale ◽

Feature Space ◽

Rna Seq ◽

Computational Framework ◽

Learning Techniques ◽

Multiple Feature ◽

The Many ◽

Cell Data

ABSTRACTEpigenetic single-cell measurements reveal a layer of regulatory information not accessible to single-cell transcriptomics, however single-cell-omics analysis tools mainly focus on gene expression data. To address this issue, we present epiScanpy, a computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data. EpiScanpy makes the many existing RNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities. We introduce and compare multiple feature space constructions for epigenetic data and show the feasibility of common clustering, dimension reduction and trajectory learning techniques. We benchmark epiScanpy by interrogating different single-cell brain mouse atlases of DNA methylation, ATAC-seq and transcriptomics. We find that differentially methylated and differentially open markers between cell clusters enrich transcriptome-based cell type labels by orthogonal epigenetic information.

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An Automated, Single Cell Quantitative Imaging Microscopy Approach to Assess Micronucleus Formation, Genotoxicity and Chromosome Instability

Cells ◽

10.3390/cells9020344 ◽

2020 ◽

Vol 9 (2) ◽

pp. 344 ◽

Cited By ~ 5

Author(s):

Chloe C. Lepage ◽

Laura L. Thompson ◽

Bradley Larson ◽

Kirk J. McManus

Keyword(s):

Image Analysis ◽

Single Cell ◽

Large Scale ◽

Quantitative Imaging ◽

Chromosome Instability ◽

Genotoxic Stress ◽

Automated Image Analysis ◽

Micronucleus Formation ◽

Distinct Cell ◽

Efficient Alternative

Micronuclei are small, extranuclear bodies that are distinct from the primary cell nucleus. Micronucleus formation is an aberrant event that suggests a history of genotoxic stress or chromosome mis-segregation events. Accordingly, assays evaluating micronucleus formation serve as useful tools within the fields of toxicology and oncology. Here, we describe a novel micronucleus formation assay that utilizes a high-throughput imaging platform and automated image analysis software for accurate detection and rapid quantification of micronuclei at the single cell level. We show that our image analysis parameters are capable of identifying dose-dependent increases in micronucleus formation within three distinct cell lines following treatment with two established genotoxic agents, etoposide or bleomycin. We further show that this assay detects micronuclei induced through silencing of the established chromosome instability gene, SMC1A. Thus, the micronucleus formation assay described here is a versatile and efficient alternative to more laborious cytological approaches, and greatly increases throughput, which will be particularly beneficial for large-scale chemical or genetic screens.

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Novel deep supervised ML models with feature selection approach for large-scale utilities and buildings short and medium-term load requirement forecasts

Energy ◽

10.1016/j.energy.2020.118477 ◽

2020 ◽

Vol 209 ◽

pp. 118477 ◽

Cited By ~ 2

Author(s):

Tanveer Ahmad ◽

Hongcai Zhang

Keyword(s):

Feature Selection ◽

Large Scale ◽

Medium Term ◽

Selection Approach ◽

Feature Selection Approach

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missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform

Bioinformatics ◽

10.1093/bioinformatics/btv560 ◽

2015 ◽

Vol 32 (2) ◽

pp. 286-288 ◽

Cited By ~ 190

Author(s):

Belinda Phipson ◽

Jovana Maksimovic ◽

Alicia Oshlack

Keyword(s):

Dna Methylation ◽

Cost Effective ◽

R Package ◽

Supplementary Information ◽

450K Array ◽

Illumina Humanmethylation450 ◽

Bioconductor Project ◽

Illumina Humanmethylation450 Beadchip ◽

Differential Variability ◽

Differential Methylation Analysis

Abstract Summary: DNA methylation is one of the most commonly studied epigenetic modifications due to its role in both disease and development. The Illumina HumanMethylation450 BeadChip is a cost-effective way to profile >450 000 CpGs across the human genome, making it a popular platform for profiling DNA methylation. Here we introduce missMethyl, an R package with a suite of tools for performing normalization, removal of unwanted variation in differential methylation analysis, differential variability testing and gene set analysis for the 450K array. Availability and implementation: missMethyl is an R package available from the Bioconductor project at www.bioconductor.org. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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i2dash: Creation of Flexible, Interactive and Web-based Dashboards for Visualization of Omics-pipeline Results

10.1101/2020.07.06.189563 ◽

2020 ◽

Author(s):

Arsenij Ustjanzew ◽

Jens Preussner ◽

Mette Bentsen ◽

Carsten Kuenne ◽

Mario Looso

Keyword(s):

Single Cell ◽

Data Visualization ◽

Large Scale ◽

R Package ◽

Cloud Services ◽

Sequencing Analysis ◽

Omics Data ◽

Web Based ◽

Automated Data Processing ◽

Generic Design

AbstractData visualization and interactive data exploration are important aspects of illustrating complex concepts and results from analyses of omics data. A suitable visualization has to be intuitive and accessible. Web-based dashboards have become popular tools for the arrangement, consolidation and display of such visualizations. However, the combination of automated data processing pipelines handling omics data and dynamically generated, interactive dashboards is poorly solved. Here, we present i2dash, an R package intended to encapsulate functionality for programmatic creation of customized dashboards. It supports interactive and responsive (linked) visualizations across a set of predefined graphical layouts. i2dash addresses the needs of data analysts for a tool that is compatible and attachable to any R-based analysis pipeline, thereby fostering the separation of data visualization on one hand and data analysis tasks on the other hand. In addition, the generic design of i2dash enables data analysts to generate modular extensions for specific needs. As a proof of principle, we provide an extension of i2dash optimized for single-cell RNA-sequencing analysis, supporting the creation of dashboards for the visualization needs of single-cell sequencing experiments. Equipped with these features, i2dash is suitable for extensive use in large scale sequencing/bioinformatics facilities. Along this line, we provide i2dash as a containerized solution, enabling a straightforward large-scale deployment and sharing of dashboards using cloud services.i2dash is freely available via the R package archive CRAN.

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Multi-resolution characterization of molecular taxonomies in bulk and single-cell transcriptomics data

10.1101/2020.11.05.370197 ◽

2020 ◽

Author(s):

Eric R. Reed ◽

Stefano Monti

Keyword(s):

Breast Cancer ◽

Single Cell ◽

Large Scale ◽

Recursive Partitioning ◽

A Priori ◽

Cost Effective ◽

R Package ◽

Cancer Tissue ◽

Data Types ◽

Transcriptomics Data

AbstractAs high-throughput genomics assays become more efficient and cost effective, their utilization has become standard in large-scale biomedical projects. These studies are often explorative, in that relationships between samples are not explicitly defined a priori, but rather emerge from data-driven discovery and annotation of molecular subtypes, thereby informing hypotheses and independent evaluation. Here, we present K2Taxonomer, a novel unsupervised recursive partitioning algorithm and associated R package that utilize ensemble learning to identify robust subgroups in a “taxonomy-like” structure (https://github.com/montilab/K2Taxonomer). K2Taxonomer was devised to accommodate different data paradigms, and is suitable for the analysis of both bulk and single-cell transcriptomics data. For each of these data types, we demonstrate the power of K2Taxonomer to discover known relationships in both simulated and human tissue data. We conclude with a practical application on breast cancer tumor infiltrating lymphocyte (TIL) single-cell profiles, in which we identified co-expression of translational machinery genes as a dominant transcriptional program shared by T cells subtypes, associated with better prognosis in breast cancer tissue bulk expression data.

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MINI REVIEW: Statistical methods for detecting differentially methylated loci and regions

10.1101/007120 ◽

2014 ◽

Cited By ~ 2

Author(s):

Mark D Robinson ◽

Abdullah Kahraman ◽

Charity W Law ◽

Helen Lindsay ◽

Malgorzata Nowicka ◽

...

Keyword(s):

Dna Methylation ◽

Statistical Methods ◽

Empirical Bayes ◽

Large Scale ◽

Rapid Development ◽

Methylation Status ◽

Regulation Of Gene Expression ◽

Data Types ◽

Cpg Dinucleotides ◽

Reversible Addition

DNA methylation, and specifically the reversible addition of methyl groups at CpG dinucleotides genome-wide, represents an important layer that is associated with the regulation of gene expression. In particular, aberrations in the methylation status have been noted across a diverse set of pathological states, including cancer. With the rapid development and uptake of large scale sequencing of short DNA fragments, there has been an explosion of data analytic methods for processing and discovering changes in DNA methylation across diverse data types. In this mini-review, we aim to condense many of the salient challenges, such as experimental design, statistical methods for differential methylation detection and critical considerations such as cell type composition and the potential confounding that can arise from batch effects, into a compact and accessible format. Our main interests, from a statistical perspective, include the practical use of empirical Bayes or hierarchical models, which have been shown to be immensely powerful and flexible in genomics and the procedures by which control of false discoveries are made. Of course, there are many critical platform-specific data preprocessing aspects that we do not discuss here. In addition, we do not make formal performance comparisons of the methods, but rather describe the commonly used statistical models and many of the pertinent issues; we make some recommendations for further study.

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SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

10.1101/118901 ◽

2017 ◽

Cited By ~ 9

Author(s):

Bo Wang ◽

Daniele Ramazzotti ◽

Luca De Sano ◽

Junjie Zhu ◽

Emma Pierson ◽

...

Keyword(s):

Single Cell ◽

Large Scale ◽

Single Cell Analysis ◽

R Package ◽

Supplementary Information ◽

Cell Analysis ◽

Rna Seq ◽

A Cell ◽

Supplementary Material ◽

Public Datasets

AbstractMotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.Availability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on [email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.

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