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.

DEMOC: a deep embedded multi-omics learning approach for clustering single-cell CITE-seq data.

Advances in single-cell RNA sequencing (scRNA-seq) technologies has provided an unprecedent opportunity for cell-type identification. As clustering is an effective strategy towards cell-type identification, various computational approaches have been proposed for clustering scRNA-seq data. Recently, with the emergence of cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), the cell surface expression of specific proteins and the RNA expression on the same cell can be captured, which provides more comprehensive information for cell analysis. However, existing single cell clustering algorithms are mainly designed for single-omic data, and have difficulties in handling multi-omics data with diverse characteristics efficiently. In this study, we propose a novel deep embedded multi-omics clustering with collaborative training (DEMOC) model to perform joint clustering on CITE-seq data. Our model can take into account the characteristics of transcriptomic and proteomic data, and make use of the consistent and complementary information provided by different data sources effectively. Experiment results on two real CITE-seq datasets demonstrate that our DEMOC model not only outperforms state-of-the-art single-omic clustering methods, but also achieves better and more stable performance than existing multi-omics clustering methods. We also apply our model on three scRNA-seq datasets to assess the performance of our model in rare cell-type identification, novel cell-subtype detection and cellular heterogeneity analysis. Experiment results illustrate the effectiveness of our model in discovering the underlying patterns of data.

Diversity and potential functional characteristics of phage communities colonizing microplastic biofilms.

The multiple ecological influences and potential microbial degradation of microplastics are generally attributed to the microbial communities colonized on microplastics. Phages play an important role in the composition and function of their bacterial hosts, yet the occurrence and the potential functional characteristics of phages in the biofilms of microplastics have not been known. This study, for the first time, explored the diversity, composition, and potential function characteristics of phage communities living in the biofilms of PP, PE, and PET microplastics and stones, cultured in the same site, via the metagenome method. The results showed that a total of 240 non-redundant virus OTUs (vOTUs), distributed in at least four orders and seven families, were detected from biofilm metagenomes of microplastics. Compared to stones, some phages were selectively enriched by microplastic biofilms, with 13 vOTUs uniquely colonized on three microplastics, and these vOTUs mainly belong to the family Autographiviridae and Podoviridae. Except for the evenness of PP, the richness index, Chao 1 index, and abundance of phage communities of three microplastics were much higher than that of stone. At least 8 bacterial phyla and 72 genera were possibly infected by phages. Compared to the stones, both composition and abundance of the phages and hosts presented significant and strong correlations for three microplastics. Some of the bacterial hosts on microplastics were likely involved in the microplastic degradation, fermenters, nitrogen transformation processes, and so on. A total of 124 encoding auxiliary metabolic genes (AMGs) were detected from viral contigs. The abundance of AMGs in microplastics was much higher than that of stones, which may provide more direct or indirect support for the bacterial degradation of microplastics. This study provides a new perspective on the occurrence and potential functions of phages on microplastic biofilms, thus expanding our understanding of microbial communities on microplastic biofilms.

Depth induced assembly discrepancy of multitrophic microbial communities affect microbial nitrogen transformation processes in river cross-sections.

Understanding how the structures and functions of bacterial and microeukaryotic communities vary within cross-sections will improve managements aimed at restoring river ecological functions. However, no comprehensive investigation has examined how microbial community characteristics vary within cross-sections, which makes the accurate calculation and prediction of microbial metabolic processing of substances in rivers difficult. Here, the distributions, co-occurrence networks, and assemblies of bacterial and microeukaryotic communities and their feedback to nitrogen transformation in cross-sections of the Yangtze River were studied by coupling ecological theory, biogeochemistry, and DNA meta-barcoding methods. The study found that depth in cross-sections was the primary driving factor regulating the composition of sediment bacterial and microeukaryotic communities. Co-occurrence network analysis indicated that the effect of bacteria on the co-occurrence network decreased and the network become more simplified and instability with depth in river cross-sections. Quantified using the ß-nearest taxon index, the H2 layer sediment (depth 10-20 m) displayed the largest variation in selection processes for microbial assemblies, while homogeneous selection and homogenizing dispersal contributed most to the bacterial and microeukaryotic assemblies in the H3 layer (depth >20 m). Cross-sectional depth and denitrification genes had a significant quadratic correlation, with the highest microbial nitrogen-removal potential occurring in the H2 layer sediment. Structural equation models showed that the sediment nitrogen distributions were regulated by distinct environmental pathways at different depths, and that the H2 layer sediment was primary driven by bacterial community. In this layer, river cross-sectional depth influenced nitrogen transformation by regulating the distribution of sediment particle sizes, which then influenced the assembly of the multitrophic microbial communities. This study will improve river management by clarifying the importance of cross-sectional depth to the ecological function of rivers.

Eutrophication dangers the ecological status of coastal wetlands: A quantitative assessment by composite microbial index of biotic integrity.

The intertidal wetland ecosystem is vulnerable to environmental and anthropogenic stressors. Understanding how the ecological statuses of intertidal wetlands respond to influencing factors is crucial for the management and protection of intertidal wetland ecosystems. In this study, the community characteristics of bacteria, archaea and microeukaryote from Jiangsu coast areas (JCA), the longest muddy intertidal wetlands in the world, were detected to develop a composite microbial index of biotic integrity (CM-IBI) and to explore the influence mechanisms of stresses on the intertidal wetland ecological status. A total of 12 bacterial, archaea and microeukaryotic metrics were determined by range, responsiveness and redundancy tests for the development of ba-IBI, ar-IBI and eu-IBI. The CM-IBI was further developed via three sub-IBIs with weight coefficients 0.40, 0.33 and 0.27, respectively. The CM-IBI (R2 = 0.58) exhibited the highest goodness of fit with the CEI, followed by ba-IBI (R2 = 0.36), ar-IBI (R2 = 0.25) and eu-IBI (R2 = 0.21). Redundancy and random forest analyses revealed inorganic nitrogen (inorgN), total phosphorus (TP) and total organic carbon (TOC) to be key environmental variables influencing community compositions. A conditional reasoning tree model indicated the close associating between the ecological status and eutrophication conditions. The majority of sites with water inorgN<0.67 mg/L exhibited good statuses, while the poor ecological status was observed for inorgN>0.67 mg/L and TP > 0.11 mg/L. Microbial networks demonstrated the interactions of microbial taxonomic units among three kingdoms decreases with the ecological degradation, suggesting a reduced reliability and stability of microbial communities. Multi-level path analysis revealed fishery aquaculture and industrial development as the dominant anthropogenic activities effecting the eutrophication and ecological degradation of the JCA tidal wetlands. This study developed an efficient ecological assessment method of tidal wetlands based on microbial communities, and determined the influence of human activities and eutrophication on ecological status, providing guidance for management standards and coastal development.