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

• URL: http://arxiv.org/abs/2112.03266v1
• Date: Sun, 5 Dec 2021 13:00:58 GMT
• Title: Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration
• Authors: Xuesong Wang (1 and 2), Zhihang Hu (1), Tingyang Yu (1), Ruijie Wang (1), Yumeng Wei (1), Juan Shu (3), Jianzhu Ma (4), Yu Li (1 and 2) ((1) Department of Computer Science and Engineering, CUHK, Hong Kong SAR, China, (2) 2The CUHK Shenzhen Research Institute, Hi-Tech Park, Nanshan, Shenzhen, 518057, China, (3) Purdue University, West Lafayette, IN 47907, United States, (4) Institute for Artificial Intelligence, Peking University, Beijing, 100871, China)
• Abstract summary: We propose a novel framework to align and integrate single-cell RNA-seq data and single-cell ATAC-seq data. Compared with the other state-of-the-art methods, our method performs better in both simulated and real single-cell data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data [10.429856767305687]
  We propose a novel probabilistic learning framework that explicitly incorporates conditional independence relationships between multi-modal data. We demonstrate the versatility of our framework across various applications pertinent to single-cell multi-omics data integration.
  arXiv  Detail & Related papers  (2024-05-28T23:44:09Z)
• Single-cell Multi-view Clustering via Community Detection with Unknown Number of Clusters [64.31109141089598]
  We introduce scUNC, an innovative multi-view clustering approach tailored for single-cell data. scUNC seamlessly integrates information from different views without the need for a predefined number of clusters. We conducted a comprehensive evaluation of scUNC using three distinct single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-28T08:34:58Z)
• Mixed Models with Multiple Instance Learning [51.440557223100164]
  We introduce MixMIL, a framework integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance Learning (MIL) Our empirical results reveal that MixMIL outperforms existing MIL models in single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-04T16:42:42Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Is your data alignable? Principled and interpretable alignability testing and integration of single-cell data [24.457344926393397]
  Single-cell data integration can provide a comprehensive molecular view of cells. Existing methods suffer from several fundamental limitations. We present a spectral manifold alignment and inference framework.
  arXiv  Detail & Related papers  (2023-08-03T16:04:14Z)
• CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data [47.2764293508916]
  Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. One obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. We propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention.
  arXiv  Detail & Related papers  (2023-04-12T00:22:18Z)
• MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy [9.405458160620533]
  We propose MEDIAR, a holistic pipeline for cell instance segmentation under multi-modality. We achieve a 0.9067 F1-score at the validation phase while satisfying the time budget. To facilitate subsequent research, we provide the source code and trained model as open-source.
  arXiv  Detail & Related papers  (2022-12-07T05:09:24Z)
• AVIDA: Alternating method for Visualizing and Integrating Data [1.6637373649145604]
  AVIDA is a framework for simultaneously performing data alignment and dimension reduction. We show that AVIDA correctly aligns high-dimensional datasets without common features. In general applications, other methods can be used for the alignment and dimension reduction modules.
  arXiv  Detail & Related papers  (2022-05-31T22:36:10Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.