Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data

Zuo, Chunman; Dai, Hao; Chen, Luonan

doi:10.1093/bioinformatics/btab403

Cited by 40 publications

(33 citation statements)

References 35 publications

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“…The two data sets contain a SNARE-seq ( Jin et al, 2020 ) CellLine data set and a SHARE-seq ( Ma et al, 2020 ) Ma data set. The competing methods are, namely, Seurat V4, MOFA+, scAI, scMVAE ( Zuo and Chen, 2021 ), and DCCA ( Zuo et al, 2021 ). The performance of each method was evaluated by evaluation index NMI and ARI.…”

Section: Resultsmentioning

confidence: 99%

Clustering CITE-seq data with a canonical correlation-based deep learning method

2022

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Single-cell multiomics sequencing techniques have rapidly developed in the past few years. Among these techniques, single-cell cellular indexing of transcriptomes and epitopes (CITE-seq) allows simultaneous quantification of gene expression and surface proteins. Clustering CITE-seq data have the great potential of providing us with a more comprehensive and in-depth view of cell states and interactions. However, CITE-seq data inherit the properties of scRNA-seq data, being noisy, large-dimensional, and highly sparse. Moreover, representations of RNA and surface protein are sometimes with low correlation and contribute divergently to the clustering object. To overcome these obstacles and find a combined representation well suited for clustering, we proposed scCTClust for multiomics data, especially CITE-seq data, and clustering analysis. Two omics-specific neural networks are introduced to extract cluster information from omics data. A deep canonical correlation method is adopted to find the maximumly correlated representations of two omics. A novel decentralized clustering method is utilized over the linear combination of latent representations of two omics. The fusion weights which can account for contributions of omics to clustering are adaptively updated during training. Extensive experiments over both simulated and real CITE-seq data sets demonstrated the power of scCTClust. We also applied scCTClust on transcriptome–epigenome data to illustrate its potential for generalizing.

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Section: Resultsmentioning

confidence: 99%

Clustering CITE-seq data with a canonical correlation-based deep learning method

2022

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“…4 and Figure 1 E. More generally, this concept is based on a cycle GAN [71] and is also present in, e.g., Khan et al [26], Wang et al [61], Xu et al [65], Zhao et al [70] and Zuo et al [73].…”

Section: Approaches For Paired Datamentioning

confidence: 93%

“…This corresponds to the idea of cyclical adversarial training as described in Section 2.4 and Figure 1 E. More generally, this concept is based on a cycle GAN [66] and is also present in, e.g., Khan et al [24], Wang et al [56], Xu et al [60], Zhao et al [65] and Zuo et al [68].…”

Section: Literature Reviewmentioning

confidence: 99%

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

Brombacher

Hackenberg

Kreutz

et al. 2022

Preprint

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Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns via a joint embedding. Yet, this also raises the question of sample size requirements for identifying such patterns from single-cell multi-omics data. Here, we empirically examine the quality of DGM-based integrations for varying sample sizes. We first review the literature and give a short overview of deep learning methods for multi-omics integration. Next, we consider six popular tools in more detail and examine their robustness to different cell numbers, covering two of the most common multi-omics types currently favored. Specifically, we use data featuring simultaneous gene expression measurements at the RNA level and protein abundance measurements for cell surface proteins (CITE-seq), as well as data where chromatin accessibility and RNA expression are measured in thousands of cells (10x Multiome). We examine the ability of the methods to learn joint embeddings based on biological and technical metrics. Finally, we provide recommendations for the design of multi-omics experiments and discuss potential future developments.

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“…With the development of deep learning, many neural-network approaches have been proposed and demonstrated powerful in data integration across modalities. Most current neural-network methods are based on autoencoder and require paired-cell datasets, such as DCCA [15] and Cobolt [16], to utilize cell-paring information. However, when cell-paring information is unavailable, simultaneously training different autoencoders and aligning cells across different modalities in latent space still make computation challenging exercise.…”

Section: Introductionmentioning

confidence: 99%

uniPort: a unified computational framework for single-cell data integration with optimal transport

Cao

Gong

et al. 2022

Preprint

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Single-cell data integration can provide a comprehensive molecular view of cells. Here we introduce uniPort, a unified single-cell data integration framework which combines a coupled Variational Autoencoder (coupled-VAE) and Minibatch Unbalanced Optimal Transport (Minibatch-UOT). It leverages both highly variable common and dataset-specific genes for integration and is scalable to large-scale and partially overlapping datasets. uniPort jointly embeds heterogeneous single-cell multi-omics datasets into a shared latent space. It can further construct a reference atlas for online prediction across datasets. Meanwhile, uniPort provides a flexible label transfer framework to deconvolute spatial heterogeneous data using optimal transport space, instead of embedding latent space. We demonstrate the capability of uniPort by integrating a variety of datasets, including single-cell transcriptomics, chromatin accessibility and spatially resolved transcriptome data. uniPort software is available at https://github.com/caokai1073/uniPort.

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Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data

Cited by 40 publications

References 35 publications

Clustering CITE-seq data with a canonical correlation-based deep learning method

Clustering CITE-seq data with a canonical correlation-based deep learning method

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data

uniPort: a unified computational framework for single-cell data integration with optimal transport

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