Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen

Z, Li; Kuppe, Christoph; Ziegler, Susanne; Cheng, Mingbo; Kabgani, Nazanin; Menzel, Sylvia; Zenke, Martin; Kramann, Rafael; Costa, Ivan G.

doi:10.1038/s41467-021-26530-2

Cited by 71 publications

(74 citation statements)

References 81 publications

(164 reference statements)

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“…After choosing bins or peaks, some methods assign the feature counts based on the number of fragments that overlap with a region (fragment-based counting; e.g., Signac 1 and snapATAC 2 ), while others assign counts based on the number of insertions within the region (insertion-based counting; e.g., 10X cellranger ATAC 3 and ArchR 4 ). After feature counting, most methods convert the counts into a binary state of “open” or “closed” (e.g., snapATAC 2 , SCALE 5 , scOPEN 6 , MASETRO 7 , and cisTopic 8 ), while other retain quantitative count information, implying that single nucleus assays may contain quantitative information on nucleosome density or turnover (e.g., scABC 9 , chromVAR 10 , and ArchR 4 ).…”

Section: Mainmentioning

confidence: 99%

Is single nucleus ATAC-seq accessibility a qualitative or quantitative measurement?

Miao

Kim

2022

Preprint

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Single nucleus ATAC-seq is a key assay for gene regulation analysis. Existing approaches to scoring feature matrices from sequencing reads are inconsistent with each other, creating differences in downstream analysis, and displaying artifacts. We show that even with sparse single cell data, quantitative counts are informative for estimating a cell’s regulatory state, which calls for consistent treatment. We propose Paired-Insertion-Counting (PIC) as a uniform method for snATAC-seq feature characterization.

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Section: Mainmentioning

confidence: 99%

Is single nucleus ATAC-seq accessibility a qualitative or quantitative measurement?

Miao

Kim

2022

Preprint

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“…Suppose that we have one omics data with known cell types, and we want to analyze other omics data from the same population of cells without the label information. For instance, there are lots of work for RNA-seq clustering (9, 37, 38, 39), but little work for ATAC-seq clustering since the sparsity of ATAC-seq poses challenges on computational methods (40). Although obtaining scATAC-seq data from cells is becoming simpler and less expensive (41), getting the clustering information is not an easy task.…”

Section: Resultsmentioning

confidence: 99%

Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration

Wang

et al. 2021

Preprint

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Muilti-modality data are ubiquitous in biology, especially that we have entered the multi-omics era, when we can measure the same biological object (cell) from different aspects (omics) to provide a more comprehensive insight into the cellular system. When dealing with such multi-omics data, the first step is to determine the correspondence among different modalities. In other words, we should match data from different spaces corresponding to the same object. This problem is particularly challenging in the single-cell multi-omics scenario because such data are very sparse with extremely high dimensions. Secondly, matched single-cell multi-omics data are rare and hard to collect. Furthermore, due to the limitations of the experimental environment, the data are usually highly noisy. To promote the single-cell multi-omics research, we overcome the above challenges, proposing a novel framework to align and integrate single-cell RNA-seq data and single-cell ATAC-seq data. Our approach can efficiently map the above data with high sparsity and noise from different spaces to a low-dimensional manifold in a unified space, making the downstream alignment and integration straightforward. Compared with the other state-of-the-art methods, our method performs better in both simulated and real single-cell data. The proposed method is helpful for the single-cell multi-omics research. The improvement for integration on the simulated data is significant.

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“…However, data produced by each modality has quite distinct characteristics regarding their numerical values (e.g. low counts for open chromatin and variable count values for RNA and proteins levels), dimensionality (dozens for proteins, tens of thousands for genes, hundreds of thousands for open chromatin) and levels of data sparsity ( Argelaguet et al , 2021 ; Li et al , 2021 ). These make the integrative analysis of multi-modal data a challenging task.…”

Section: Introductionmentioning

confidence: 99%

MOJITOO: a fast and universal method for integration of multimodal single-cell data

Cheng

Costa

2022

Bioinformatics

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Motivation The advent of multi-modal single-cell sequencing techniques have shed new light on molecular mechanisms by simultaneously inspecting transcriptomes, epigenomes and proteomes of the same cell. However, to date, the existing computational approaches for integration of multimodal single-cell data are either computationally expensive, require the delineation of parameters or can only be applied to particular modalities. Results Here we present a single-cell multi-modal integration method, named Multi-mOdal Joint IntegraTion of cOmpOnents (MOJITOO). MOJITOO uses canonical correlation analysis for a fast and parameter free detection of a shared representation of cells from multimodal single-cell data. Moreover, estimated canonical components can be used for interpretation, i.e. association of modality-specific molecular features with the latent space. We evaluate MOJITOO using bi- and tri-modal single-cell datasets and show that MOJITOO outperforms existing methods regarding computational requirements, preservation of original latent spaces and clustering. Availability and implementation The software, code and data for benchmarking are available at https://github.com/CostaLab/MOJITOO and https://doi.org/10.5281/zenodo.6348128. Supplementary information Supplementary data are available at Bioinformatics online.

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Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen

Cited by 71 publications

References 81 publications

Is single nucleus ATAC-seq accessibility a qualitative or quantitative measurement?

Is single nucleus ATAC-seq accessibility a qualitative or quantitative measurement?

Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration

MOJITOO: a fast and universal method for integration of multimodal single-cell data

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