Clustering single-cell multi-omics data via graph regularized multi-view ensemble learning.

MOTIVATION: Single-cell clustering plays a crucial role in distinguishing between cell types, facilitating the analysis of cell heterogeneity mechanisms. While many existing clustering methods rely solely on gene expression data obtained from single-cell RNA sequencing techniques to identify cell clusters, the information contained in mono-omic data is often limited, leading to suboptimal clustering performance. The emergence of single-cell multi-omics sequencing technologies enables the integration of multiple omics data for identifying cell clusters, but how to integrate different omics data effectively remains challenging. In addition, designing a clustering method that performs well across various types of multi-omics data poses a persistent challenge due to the data's inherent characteristics. RESULTS: In this paper, we propose a graph-regularized multi-view ensemble clustering (GRMEC-SC) model for single-cell clustering. Our proposed approach can adaptively integrate multiple omics data and leverage insights from multiple base clustering results. We extensively evaluate our method on five multi-omics datasets through a series of rigorous experiments. The results of these experiments demonstrate that our GRMEC-SC model achieves competitive performance across diverse multi-omics datasets with varying characteristics. AVAILABILITY AND IMPLEMENTATION: Implementation of GRMEC-SC, along with examples, can be found on the GitHub repository: https://github.com/polarisChen/GRMEC-SC.

An accurate and convenient method for Mycoplasma pneumoniae via one-step LAMP-CRISPR/Cas12b detection platform.

Introduction: Mycoplasma pneumoniae (MP) is the major cause of respiratory infections that threaten the health of children and adolescents worldwide. Therefore, an early, simple, and accurate detection approach for MP is critical to prevent outbreaks of MP-induced community-acquired pneumonia. Methods: Here, we explored a simple and accurate method for MP identification that combines loop-mediated isothermal amplification (LAMP) with the CRISPR/Cas12b assay in a one-pot reaction. Results: In the current study, the whole reaction was completed within 1 h at a constant temperature of 57°C. The limit of detection of this assay was 33.7 copies per reaction. The specificity of the LAMP-CRISPR/Cas12b method was 100%, without any cross-reactivity with other pathogens. Overall, 272 clinical samples were used to evaluate the clinical performance of LAMP-CRISPR/Cas12b. Compared with the gold standard results from real-time PCR, the present method provided a sensitivity of 88.11% (126/143), specificity of 100% (129/129), and consistency of 93.75% (255/272). Discussion: Taken together, our preliminary results illustrate that the LAMP-CRISPR/Cas12b method is a simple and reliable tool for MP diagnosis that can be performed in resource-limited regions.