SHARE-seq reveals chromatin potential

Clyde, Dorothy

doi:10.1038/s41576-020-00308-6

Cited by 8 publications

(6 citation statements)

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“…MoClust can also be applied on scRNA-seq and scEpigenome data for integrative analysis. We examined MoClust on three published datasets, including SNARE-seq datasets CellLine and 10XPBMC, as well as SHARE-seq ( Clyde, 2020 ) dataset Ma-2020. The detailed information of these datasets is listed in the Supplementary Information .…”

Section: Resultsmentioning

confidence: 99%

Clustering single-cell multi-omics data with MoClust

2022

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Motivation Single-cell multi-omics sequencing techniques have rapidly developed in the past few years. Clustering analysis with single-cell multi-omics data may give us novel perspectives to dissect cellular heterogeneity. However, multi-omics data have the properties of inherited large dimension, high sparsity and existence of doublets. Moreover, representations of different omics from even the same cell follow diverse distributions. Without proper distribution alignment techniques, clustering methods will encounter less separable clusters easily affected by less informative omics data. Results We developed MoClust, a novel joint clustering framework that can be applied to several types of single-cell multi-omics data. A selective automatic doublet detection module that can identify and filter out doublets is introduced in the pretraining stage to improve data quality. Omics-specific autoencoders are introduced to characterize the multi-omics data. A contrastive learning way of distribution alignment is adopted to adaptively fuse omics representations into an omics-invariant representation. This novel way of alignment boosts the compactness and separableness of clusters, while accurately weighting the contribution of each omics to the clustering object. Extensive experiments, over both simulated and real multi-omics datasets, demonstrated the powerful alignment, doublet detection and clustering ability features of MoClust. Availability An implementation of MoClust is available from https://doi.org/10.5281/zenodo.7306504 Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Clustering single-cell multi-omics data with MoClust

2022

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“…A PREPRINT -FEBRUARY 17, 2024 SNARE-seq [12] and SHARE-seq [13] (joint measurement of chromatin accessibility and transcriptome in single-cells or nuclei). All these technologies facilitate deeper understanding of the cellular identity and processes in organs and tissues.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, joint sequencing technologies have come up, which allow simultaneous measurement of more than one modalities thereby offering multiple views of the same cells of interest. These technologies include CITE-seq [6], REAP-seq [7] (joint measurement of proteins and transcriptome), scM&T-seq [8], scMT-seq [9], scTrio-seq [10] (simultaneous measurement of methylome and trascriptome), and sci-CAR [11], SNARE-seq [12] and SHARE-seq [13] (joint measurement of chromatin accessibility and transcriptome in single-cells or nuclei). All these technologies facilitate deeper understanding of the cellular identity and processes in organs and tissues.…”

Section: Introductionmentioning

confidence: 99%

NeuroMDAVIS: Visualization of single-cell multi-omics data under deep learning framework

Maitra,

Seal,

Das

et al. 2024

Preprint

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Single-cell technologies have favoured extensive advancements in cell-type discovery, cell state identi-fication, development of lineage tracing and disease understanding among others. Further, single-cell multi-omics data generated using modern technologies provide several views of omics contribution for the same set of cells. However, dimension reduction and visualization of biological datasets (single or multi-omics) remain a challenging task since obtaining a low-dimensional embedding that preserves information about local and global structures in data, is difficult. Further, combining different views obtained from each omics layer to interpret the underlying biology is even more challenging. Earlier, we have developed NeuroDAVIS which can perform the task of visualization of high-dimensional datasets of a single modality while preserving cluster-structures within the data. Nevertheless, there is no model so far that supports joint visualization of multi-omics datasets. Joint visualization refers to transforming the feature space of each individual modality and combining them to produce a latent embedding that supports visualization of the multi-modal dataset in the newly transformed feature space. In this work, we introduce NeuroMDAVIS which is a generalized version of NeuroDAVIS for visualization of biological datasets having multiple modalities. To the best of our knowledge, NeuroMDAVIS is the first of its kind multi-modal data visualization model. It is able to learn both local and global relationships in the data while generating a low-dimensional embedding useful for downstream tasks. NeuroMDAVIS competes against state-of-the-art visualization models like t-Distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), Fast interpolation-based t-SNE (Fit-SNE), and the Siamese network-based visualization method (IVIS).

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“…Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) [4] and RNA expression and protein sequencing (REAP-seq) [5] measure protein and gene expression. Single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq) [5] , [6] , simultaneous high-throughput ATAC and RNA expression with sequencing (SHARE-seq) [7] , and single-cell combinatorial profiling of chromatin accessibility and mRNA (sci-CAR) [8] measure gene expression and chromatin accessibility, while single-cell sequencing of gene expression and methylation (scGEM) [9] measures gene expression and DNA methylation and genome and transcriptome sequencing (G&T-seq) [10] assays genome and transcriptome. For triple-omics data generation, single-cell nucleosome, methylation, and transcription sequencing (scNMT) [11] measures gene expression, chromatin accessibility, and DNA methylation, while single-cell triple-omics sequencing (scTrio-seq) [9] , [12] captures single nucleotide polymorphisms (SNPs), gene expression, and DNA methylation simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Computational Methods for Single-Cell Multi-Omics Integration and Alignment

Stanojevic

Ristivojevic

et al. 2022

Genomics, Proteomics &Amp; Bioinformatics

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Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology. Multi-omics assays offer even greater opportunities to understand cellular states and biological processes. The problem of integrating different omics data with very different dimensionality and statistical properties remains, however, quite challenging. A growing body of computational tools is being developed for this task, leveraging ideas ranging from machine translation to the theory of networks, and represents another frontier on the interface of biology and data science. Our goal in this review is to provide a comprehensive, up-to-date survey of computational techniques for the integration of single-cell multi-omics data, while making the concepts behind each algorithm approachable to a non-expert audience.

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SHARE-seq reveals chromatin potential

Cited by 8 publications

References 1 publication

Clustering single-cell multi-omics data with MoClust

Clustering single-cell multi-omics data with MoClust

NeuroMDAVIS: Visualization of single-cell multi-omics data under deep learning framework

Computational Methods for Single-Cell Multi-Omics Integration and Alignment

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