Orthogonal multimodality integration and clustering in single-cell data

Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The challenges in multi-omics data analysis lie in the complexity, high dimensionality, and heterogeneity of the data, which demands sophisticated computational tools and visualization methods for proper interpretation and visualization of multi-omics data. In this paper, we propose a novel method, termed Orthogonal Multimodality Integration and Clustering (OMIC), for analyzing CITE-seq. Our approach enables researchers to integrate multiple sources of information while accounting for the dependence among them. We demonstrate the effectiveness of our approach using CITE-seq data sets for cell clustering. Our results show that our approach outperforms existing methods in terms of accuracy, computational efficiency, and interpretability. We conclude that our proposed OMIC method provides a powerful tool for multimodal data analysis that greatly improves the feasibility and reliability of integrated data.

The free energy landscape of small-world networks of cells

The properties of organs, tissues, organoids, and other systems of cells, are influenced by the spatial localization and distribution of their elements. Here, we used networks to describe distributions of cells on a surface where the small-world coefficient (SW) of the networks was varied between SW~1 (random uniform distributions) and SW~10 (clustered distributions). The small-world coefficient is a topological measure of graphs: networks with SW>1 are topologically biased to transmit information. For each system configuration, we then determined the total energy U as the sum of the energies that describe cell-cell interactions - approximated by a harmonic potential. The graph of energy (U) across the configuration space of the networks (SW) is the energy landscape: it indicates which configuration a system of cells will likely assume over time. We found that, depending on the model parameters, the energy landscapes of 2D distributions of cells may be of different types: from type I to type IV. Type I and type II systems have high probability to evolve into random distributions. Type III and type IV systems have a higher probability to form clustered architectures. A great many of simulations indicated that cultures of cells with high initial density and limited sensing range could evolve into clustered configurations with enhanced topological characteristics. Moreover, the strongest the binding between cells, the greater the likelihood that they will assume configurations characterized by finite values of SW. Results of the work are relevant for those working the field of tissue engineering, regenerative medicine, the formation of in-vitro-models, the analysis of neuro-degenerative diseases.

A mathematical model of cell movement and clustering due to chemotaxis

This paper presents a numerical method for modelling cell migration and aggregation due to chemotaxis where the cell is attracted towards the direction in which the concentration of a chemical signal is increasing. In the model presented here, each cell is represented by a system of springs connected together at node points on the cell's membrane and on the boundary of the cell's nucleus. The nodes located on a cell's membrane are subject to a force which is proportional to the gradient of the concentration of the chemical signal which mimics the behaviour of the chemical receptors in the cell's membrane. In particular, the model developed here will consider what happens when two (or more) cells collide and how their membranes connect to each other to form clusters of cells. The methods described in this paper will be illustrated with a number of typical examples simulating cells moving in response to a chemical signal and how they combine to form clusters.

DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection

BACKGROUND

Single-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification.

RESULTS

Here, we present DISCERN, a novel deep generative network that precisely reconstructs missing single-cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We show that DISCERN is robust against differences between batches and is able to keep biological differences between batches, which is a common problem for imputation and batch correction algorithms. We use DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4+ and CD8+ Tc2 T helper cells, with a potential role in adverse disease outcome. We utilize T cell fraction information of patient blood to classify mild or severe COVID-19 with an AUROC of 80% that can serve as a biomarker of disease stage. DISCERN can be easily integrated into existing single-cell sequencing workflow.

CONCLUSIONS

Thus, DISCERN is a flexible tool for reconstructing missing single-cell gene expression using a reference dataset and can easily be applied to a variety of data sets yielding novel insights, e.g., into disease mechanisms.

A scalable unsupervised learning of scRNAseq data detects rare cells through integration of structure-preserving embedding, clustering and outlier detection

Single-cell RNA-seq analysis has become a powerful tool to analyse the transcriptomes of individual cells. In turn, it has fostered the possibility of screening thousands of single cells in parallel. Thus, contrary to the traditional bulk measurements that only paint a macroscopic picture, gene measurements at the cell level aid researchers in studying different tissues and organs at various stages. However, accurate clustering methods for such high-dimensional data remain exiguous and a persistent challenge in this domain. Of late, several methods and techniques have been promulgated to address this issue. In this article, we propose a novel framework for clustering large-scale single-cell data and subsequently identifying the rare-cell sub-populations. To handle such sparse, high-dimensional data, we leverage PaCMAP (Pairwise Controlled Manifold Approximation), a feature extraction algorithm that preserves both the local and the global structures of the data and Gaussian Mixture Model to cluster single-cell data. Subsequently, we exploit Edited Nearest Neighbours sampling and Isolation Forest/One-class Support Vector Machine to identify rare-cell sub-populations. The performance of the proposed method is validated using the publicly available datasets with varying degrees of cell types and rare-cell sub-populations. On several benchmark datasets, the proposed method outperforms the existing state-of-the-art methods. The proposed method successfully identifies cell types that constitute populations ranging from 0.1 to 8% with F1-scores of 0.91 0.09. The source code is available at https://github.com/scrab017/RarPG.

scJVAE: A novel method for integrative analysis of multimodal single-cell data

The study of cellular decision-making can be approached comprehensively using multimodal single-cell omics technology. Recent advances in multimodal single-cell technology have enabled simultaneous profiling of more than one modality from the same cell, providing more significant insights into cell characteristics. However, learning the joint representation of multimodal single-cell data is challenging due to batch effects. Here we present a novel method, scJVAE (single-cell Joint Variational AutoEncoder), for batch effect removal and joint representation of multimodal single-cell data. The scJVAE integrates and learns joint embedding of paired scRNA-seq and scATAC-seq data modalities. We evaluate and demonstrate the ability of scJVAE to remove batch effects using various datasets with paired gene expression and open chromatin. We also consider scJVAE for downstream analysis, such as lower dimensional representation, cell-type clustering, and time and memory requirement. We find scJVAE a robust and scalable method outperforming existing state-of-the-art batch effect removal and integration methods.

Single-cell RNA-seq data analysis based on directed graph neural network

Single-cell RNA sequencing (scRNA-seq) data scale surges with high-throughput sequencing technology development. However, although single-cell data analysis is a powerful tool, various issues have been reported, such as sequencing sparsity and complex differential patterns in gene expression. Statistical or traditional machine learning methods are inefficient, and the accuracy needs to be improved. The methods based on deep learning can not directly process non-Euclidean spatial data, such as cell diagrams. In this study, we have developed graph autoencoders and graph attention network for scRNA-seq analysis based on a directed graph neural network named scDGAE. Directed graph neural networks cannot only retain the connection properties of the directed graph but also expand the receptive field of the convolution operation. Cosine similarity, median L1 distance, and root-mean-squared error are used to measure the gene imputation performance of different methods with scDGAE. Furthermore, adjusted mutual information, normalized mutual information, completeness score, and Silhouette coefficient score are used to measure the cell clustering performance of different methods with scDGAE. Experiment results show that the scDGAE model achieves promising performance in gene imputation and cell clustering prediction on four scRNA-seq data sets with gold-standard cell labels. Furthermore, it is a robust framework that can be applied to general scRNA-Seq analyses.

Joint analysis of scATAC-seq datasets using epiConv

Background

Technical improvement in ATAC-seq makes it possible for high throughput profiling the chromatin states of single cells. However, data from multiple sources frequently show strong technical variations, which is referred to as batch effects. In order to perform joint analysis across multiple datasets, specialized method is required to remove technical variations between datasets while keep biological information.

Results

Here we present an algorithm named epiConv to perform joint analyses on scATAC-seq datasets. We first show that epiConv better corrects batch effects and is less prone to over-fitting problem than existing methods on a collection of PBMC datasets. In a collection of mouse brain data, we show that epiConv is capable of aligning low-depth scATAC-Seq from co-assay data (simultaneous profiling of transcriptome and chromatin) onto high-quality ATAC-seq reference and increasing the resolution of chromatin profiles of co-assay data. Finally, we show that epiConv can be used to integrate cells from different biological conditions (T cells in normal vs. germ-free mouse; normal vs. malignant hematopoiesis), which reveals hidden cell populations that would otherwise be undetectable.

Conclusions

In this study, we introduce epiConv to integrate multiple scATAC-seq datasets and perform joint analysis on them. Through several case studies, we show that epiConv removes the batch effects and retains the biological signal. Moreover, joint analysis across multiple datasets improves the performance of clustering and differentially accessible peak calling, especially when the biological signal is weak in single dataset.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04858-w.

Evaluating the performance of dropout imputation and clustering methods for single-cell RNA sequencing data

Recent advances in single-cell RNA sequencing (scRNA-seq) provide exciting opportunities for transcriptome analysis at single-cell resolution. Clustering individual cells is a key step to reveal cell subtypes and infer cell lineage in scRNA-seq analysis. Although many dedicated algorithms have been proposed, clustering quality remains a computational challenge for scRNA-seq data, which is exacerbated by inflated zero counts due to various technical noise. To address this challenge, we assess the combinations of nine popular dropout imputation methods and eight clustering methods on a collection of 10 well-annotated scRNA-seq datasets with different sample sizes. Our results show that (i) imputation algorithms do typically improve the performance of clustering methods, and the quality of data visualization using t-Distributed Stochastic Neighbor Embedding; and (ii) the performance of a particular combination of imputation and clustering methods varies with dataset size. For example, the combination of single-cell analysis via expression recovery and Sparse Subspace Clustering (SSC) methods usually works well on smaller datasets, while the combination of adaptively-thresholded low-rank approximation and single-cell interpretation via multikernel learning (SIMLR) usually achieves the best performance on larger datasets.

Cell aggregation on nanorough surfaces

The ability to control adhesion and the spatial organization of cells over nanoscale surfaces is essential in tissue engineering, regenerative medicine, the growth of organoids and spheroids as an in-vitro-model of human development and disease. Nonetheless, despite the several different works that have explored the influence of nanotopography on cell adhesion and clustering, little is known about how the forces arising from membrane conformational change developing during cell adaptation to a nanorough surface, and the cell-cell adhesion forces, interact to guide cell assembly. Here, starting from the works of Decuzzi and Ferrari, who examined how the energy of a cell varies while adhering to a nanoscale surface, and of Armstrong and collaborators, who developed a continuous model of cell-cell adhesion and morphogenesis, we provide a description of how nanotopography can modulate cellular clustering. In simulations where the parameters of the model were varied over large intervals, we found that nanoroughness may induce cell aggregation from a homogenous, uniform state, also for weak cell-cell adhesion. Results of the model are relevant in bio-engineering and biomedical nanotechnology, and may be of interest for those involved in the design and fabrication of biomaterials and scaffolds for tissue formation and repair.

APEC: an accesson-based method for single-cell chromatin accessibility analysis

The development of sequencing technologies has promoted the survey of genome-wide chromatin accessibility at single-cell resolution. However, comprehensive analysis of single-cell epigenomic profiles remains a challenge. Here, we introduce an accessibility pattern-based epigenomic clustering (APEC) method, which classifies each cell by groups of accessible regions with synergistic signal patterns termed “accessons”. This python-based package greatly improves the accuracy of unsupervised single-cell clustering for many public datasets. It also predicts gene expression, identifies enriched motifs, discovers super-enhancers, and projects pseudotime trajectories. APEC is available at https://github.com/QuKunLab/APEC.

Rotating cluster mechanism for coordinated heterogeneous MIMO cellular networks

To increase the average achievable rates per user for cluster-edge users, a rotating clustering scheme for the downlink of a coordinated multicell multiuser multiple-input multiple-output system is proposed in this paper and analyzed in two network layouts. In the multicell heterogeneous cellular network, base stations of a cluster cooperate to transmit data signals to the users within the cluster; rotating cluster patterns enable all users to be nearer the cluster center in at least one of the patterns. Considering cellular layouts with three or six macrocells per site, different rotating patterns of clusters are proposed and the system performance with the proposed sets of clustering patterns is investigated using a simulated annealing algorithm for user scheduling and successive zero-forcing dirty paper coding as the precoding method. The rotating clustering scheme is less complex than fully dynamic clustering, and it is primarily designed to improve the throughput of cluster-edge users. As an extra secondary benefit, it is also capable of slightly improving the average achievable sum rate of the network overall. The effectiveness of the proposed methods with two different scheduling metrics, namely throughput maximization and proportionally fair scheduling, is of interest in this work. Moreover, the speed of rotation affects the performance of the system; the higher the speed of rotation, the more frequently any specific users will be nearer the cluster center. Our simulations demonstrate the effectiveness of the proposed rotational approach and determine the speed of rotation beyond which any additional performance gains become negligible.

A simple mathematical model of cell clustering by chemotaxis

Chemotaxis is the process by which cells and clusters of cells follow chemical signals in order to combine and form larger clusters. The spreading of the chemical signal from any given cell can be modeled using the linear diffusion equation, and the standard equations of motion can be used to determine how a cell, or cluster of cells, moves in response to the chemical signal. The resulting differential equations for the cell locations are integrated through time using the fourth-order Runge-Kutta method. The effect which changing the initial concentration magnitude, diffusion constant and velocity damping parameter has on the shape of the final clusters of cells is investigated and discussed.

Signaling via G proteins mediates tumorigenic effects of GPR87

G protein-coupled receptors (GPCRs) constitute a large protein family of seven transmembrane (7TM) spanning proteins that regulate multiple physiological functions. GPR87 is overexpressed in several cancers and plays a role in tumor cell survival. Here, the basal activity of GPR87 was investigated in transiently transfected HEK293 cells, revealing ligand-independent coupling to Gα_i, Gα_q and Gα_12/13. Furthermore, GPR87 showed a ligand-independent G protein-dependent activation of the downstream transcription factors CREB, NFκB, NFAT and SRE. In tetracycline-induced Flp-In T-Rex-293 cells, GPR87 induced cell clustering presumably through Gα_12/13 coupling. In a foci formation assay using retrovirally transduced NIH3T3 cells, GPR87 showed a strong in vitro transforming potential, which correlated to the in vivo tumor induction in nude mice. Importantly, we demonstrate that the transforming potential of GPR87 was correlated to the receptor signaling, as the signaling-impaired mutant R139A (Arg in the conserved "DRY"-motif at the bottom of transmembrane helix 3 of GPR87 substituted to Ala) showed a lower in vitro cell transformation potential. Furthermore, R139A lost the ability to induce cell clustering. In summary, we show that GPR87 is active through several signaling pathways and that the signaling activity is linked to the receptor-induced cell transformation and clustering. The robust surface expression of GPR87 and general high druggability of GPCRs make GPR87 an attractive future anticancer target for drugs that - through inhibition of the receptor signaling - will inhibit its transforming properties.

Fibroblast morphogenesis on 3D collagen matrices: the balance between cell clustering and cell migration

Fibroblast clusters have been observed in tissues under a variety of circumstances: in fibrosis and scar, in the formation of hair follicle dermal papilla, and as part of the general process of mesenchymal condensation that takes place during development. Cell clustering has been shown to depend on features of the extracellular matrix, growth factor environment, and mechanisms to stabilize cell-cell interactions. In vitro studies have shown that increasing the potential for cell-cell adhesion relative to cell-substrate adhesion promotes cell clustering. Experimental models to study fibroblast clustering have utilized centrifugation, hanging drops, and substrata with poorly adhesive, soft and mechanically unstable properties. In this review, we summarize work on a new, highly tractable, cell clustering research model in which human fibroblasts are incubated on the surfaces of collagen matrices. Fibroblast clustering occurs under procontractile growth factor conditions (e.g., serum or the serum lipid agonist lysophosphatidic acid) but not under promigratory growth factor conditions (e.g., platelet-derived growth factor) and can be reversed by switching growth factor environments. Cell contraction plays a dual role in clustering to bring cells closer together and to stimulate cells to organize fibronectin into a fibrillar matrix. Binding of fibroblasts to a shared fibronectin fibrillar matrix stabilizes clusters, and fragmentation of the fibrillar matrix occurs when growth factor conditions are switched to promote cell dispersal.