No abstract

Unsupervised methods, such as clustering methods, are essential to the analysis of singlecell genomic data. Most current clustering methods are designed for one data type only, such as scRNA-seq, scATAC-seq or sc-methylation data alone, and a few are developed for the integrative analysis of multiple data types. Integrative analysis of multimodal singlecell genomic data sets leverages the power in multiple data sets and can deepen the biological insight. We propose a coupled co-clustering-based unsupervised transfer learning algorithm (coupleCoC) for the integrative analysis of multimodal single-cell data. Our proposed coupleCoC builds upon the information theoretic co-clustering framework. We applied coupleCoC for the integrative analysis of scATAC-seq and scRNA-seq data, sc-methylation and scRNA-seq data, and scRNA-seq data from mouse and human. We demonstrate that coupleCoC improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic data sets. The software and data sets are available at https://github.com/cuhklinlab/coupleCoC

Unsupervised methods, such as clustering methods, are essential to the analysis of single-cell genomic data. The most current clustering methods are designed for one data type only, such as single-cell RNA sequencing (scRNA-seq), single-cell ATAC sequencing (scATAC-seq) or sc-methylation data alone, and a few are developed for the integrative analysis of multiple data types. The integrative analysis of multimodal single-cell genomic data sets leverages the power in multiple data sets and can deepen the biological insight. In this paper, we propose a coupled co-clustering-based unsupervised transfer learning algorithm (coupleCoC) for the integrative analysis of multimodal single-cell data. Our proposed coupleCoC builds upon the information theoretic co-clustering framework. In co-clustering, both the cells and the genomic features are simultaneously clustered. Clustering similar genomic features reduces the noise in single-cell data and facilitates transfer of knowledge across single-cell datasets. We applied coupleCoC for the integrative analysis of scATAC-seq and scRNA-seq data, sc-methylation and scRNA-seq data and scRNA-seq data from mouse and human. We demonstrate that coupleCoC improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic datasets. Our method coupleCoC is also computationally efficient and can scale up to large datasets. Availability: The software and datasets are available at https://github.com/cuhklinlab/coupleCoC.

Motion analysis of the hyoid bone via videofluoroscopic study has been used in clinical research, but the classical manual tracking method is generally labor intensive and time consuming. Although some automatic tracking methods have been developed, masked points could not be tracked and smoothing and segmentation, which are necessary for functional motion analysis prior to registration, were not provided by the previous software. We developed software to track the hyoid bone motion semi-automatically. It works even in the situation where the hyoid bone is masked by the mandible and has been validated in dysphagia patients with stroke. In addition, we added the function of semi-automatic smoothing and segmentation. A total of 30 patients’ data were used to develop the software, and data collected from 17 patients were used for validation, of which the trajectories of 8 patients were partly masked. Pearson correlation coefficients between the manual and automatic tracking are high and statistically significant (0.942 to 0.991, P-value<0.0001). Relative errors between automatic tracking and manual tracking in terms of the x-axis, y-axis and 2D range of hyoid bone excursion range from 3.3% to 9.2%. We also developed an automatic method to segment each hyoid bone trajectory into four phases (elevation phase, anterior movement phase, descending phase and returning phase). The semi-automatic hyoid bone tracking from VFSS data by our software is valid compared to the conventional manual tracking method. In addition, the ability of automatic indication to switch the automatic mode to manual mode in extreme cases and calibration without attaching the radiopaque object is convenient and useful for users. Semi-automatic smoothing and segmentation provide further information for functional motion analysis which is beneficial to further statistical analysis such as functional classification and prognostication for dysphagia. Therefore, this software could provide the researchers in the field of dysphagia with a convenient, useful, and all-in-one platform for analyzing the hyoid bone motion. Further development of our method to track the other swallowing related structures or objects such as epiglottis and bolus and to carry out the 2D curve registration may be needed for a more comprehensive functional data analysis for dysphagia with big data.

The single-cell multiomics technologies provide an unprecedented opportunity to study the cellular heterogeneity from different layers of transcriptional regulation. However, the datasets generated from these technologies tend to have high levels of noise, making data analysis challenging. Here, we propose jointly semi-orthogonal nonnegative matrix factorization (JSNMF), which is a versatile toolkit for the integrative analysis of transcriptomic and epigenomic data profiled from the same cell. JSNMF enables data visualization and clustering of the cells and also facilitates downstream analysis, including the characterization of markers and functional pathway enrichment analysis. The core of JSNMF is an unsupervised method based on JSNMF, where it assumes different latent variables for the two molecular modalities, and integrates the information of transcriptomic and epigenomic data with consensus graph fusion, which better tackles the distinct characteristics and levels of noise across different molecular modalities in single-cell multiomics data. We applied JSNMF to single-cell multiomics datasets from different tissues and different technologies. The results demonstrate the superior performance of JSNMF in clustering and data visualization of the cells. JSNMF also allows joint analysis of multiple single-cell multiomics experiments and single-cell multiomics data with more than two modalities profiled on the same cell. JSNMF also provides rich biological insight on the markers, cell-type-specific region–gene associations and the functions of the identified cell subpopulation.