Clustering Of Single-cell RNA-seq Data Research Articles

Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively.

SSNMDI: a novel joint learning model of semi-supervised non-negative matrix factorization and data imputation for clustering of single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) technology attracts extensive attention in the biomedical field. It can be used to measure gene expression and analyze the transcriptome at the single-cell level, enabling the identification of cell types based on unsupervised clustering. Data imputation and dimension reduction are conducted before clustering because scRNA-seq has a high 'dropout' rate, noise and linear inseparability. However, independence of dimension reduction, imputation and clustering cannot fully characterize the pattern of the scRNA-seq data, resulting in poor clustering performance. Herein, we propose a novel and accurate algorithm, SSNMDI, that utilizes a joint learning approach to simultaneously perform imputation, dimensionality reduction and cell clustering in a non-negative matrix factorization (NMF) framework. In addition, we integrate the cell annotation as prior information, then transform the joint learning into a semi-supervised NMF model. Through experiments on 14 datasets, we demonstrate that SSNMDI has a faster convergence speed, better dimensionality reduction performance and a more accurate cell clustering performance than previous methods, providing an accurate and robust strategy for analyzing scRNA-seq data. Biological analysis are also conducted to validate the biological significance of our method, including pseudotime analysis, gene ontology and survival analysis. We believe that we are among the first to introduce imputation, partial label information, dimension reduction and clustering to the single-cell field. The source code for SSNMDI is available at https://github.com/yushanqiu/SSNMDI.

ScGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data.

Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep-learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell-cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized. The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks and interpret the cell-cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms. scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https://github.com/OSU-BMBL/scGNN2.0. Supplementary data are available at Bioinformatics online.

Landscape and age dynamics of immune cells in the Egyptian rousette bat.

Bats harbor high-impact zoonotic viruses often in the absence of disease manifestation. This restriction and disease tolerance possibly rely on specific immunological features. In-depth molecular characterization of cellular immunity and imprinting of age on leukocyte compartments remained unexplored in bats. We employ single-cell RNA sequencing (scRNA-seq) and establish immunostaining panels to characterize the immune cell landscape in juvenile, subadult, and adult Egyptian rousette bats (ERBs). Transcriptomic and flow cytometry data reveal conserved subsets and substantial enrichments of CD79a+ B cells and CD11b+ Tcells in juvenile animals, whereas neutrophils, CD206+ myeloid cells, and CD3+ Tcells dominate as bats reach adulthood. Despite differing frequencies, phagocytosis of circulating and tissue-resident myeloid cells and proliferation of peripheral and splenic lymphocytes are analogous in juvenile and adult ERBs. We provide a comprehensive map of the immune landscape in ERBs and show age-imprinted resilience progression and find that variability in cellular immunity only partly recapitulates mammalian archetypes.

The MicroRNA miR-277 Controls Physiology and Pathology of the Adult Drosophila Midgut by Regulating the Expression of Fatty Acid β-Oxidation-Related Genes in Intestinal Stem Cells.

Cell division, growth, and differentiation are energetically costly and dependent processes. In adult stem cell-based epithelia, cellular identity seems to be coupled with a cell’s metabolic profile and vice versa. It is thus tempting to speculate that resident stem cells have a distinct metabolism, different from more committed progenitors and differentiated cells. Although investigated for many stem cell types in vitro, in vivo data of niche-residing stem cell metabolism is scarce. In adult epithelial tissues, stem cells, progenitor cells, and their progeny have very distinct functions and characteristics. In our study, we hypothesized and tested whether stem and progenitor cell types might have a distinctive metabolic profile in the intestinal lineage. Here, taking advantage of the genetically accessible adult Drosophila melanogaster intestine and the availability of ex vivo single cell sequencing data, we tested that hypothesis and investigated the metabolism of the intestinal lineage from stem cell (ISC) to differentiated epithelial cell in their native context under homeostatic conditions. Our initial in silico analysis of single cell RNAseq data and functional experiments identify the microRNA miR-277 as a posttranscriptional regulator of fatty acid β-oxidation (FAO) in the intestinal lineage. Low levels of miR-277 are detected in ISC and progressively rising miR-277 levels are found in progenitors during their growth and differentiation. Supporting this, miR-277-regulated fatty acid β-oxidation enzymes progressively declined from ISC towards more differentiated cells in our pseudotime single-cell RNAseq analysis and in functional assays on RNA and protein level. In addition, in silico clustering of single-cell RNAseq data based on metabolic genes validates that stem cells and progenitors belong to two independent clusters with well-defined metabolic characteristics. Furthermore, studying FAO genes in silico indicates that two populations of ISC exist that can be categorized in mitotically active and quiescent ISC, of which the latter relies on FAO genes. In line with an FAO dependency of ISC, forced expression of miR-277 phenocopies RNAi knockdown of FAO genes by reducing ISC size and subsequently resulting in stem cell death. We also investigated miR-277 effects on ISC in a benign and our newly developed CRISPR-Cas9-based colorectal cancer model and found effects on ISC survival, which as a consequence affects tumor growth, further underlining the importance of FAO in a pathological context. Taken together, our study provides new insights into the basal metabolic requirements of intestinal stem cell on β-oxidation of fatty acids evolutionarily implemented by a sole microRNA. Gaining knowledge about the metabolic differences and dependencies affecting the survival of two central and cancer-relevant cell populations in the fly and human intestine might reveal starting points for targeted combinatorial therapy in the hope for better treatment of colorectal cancer in the future.

A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data.

Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limited per-cell sequenced reads, cell-to-cell variability due to cell-cycle, cellular morphology, and variable reagent concentrations. Moreover, single cell data is susceptible to technical noise, which affects the quality of genes (or features) selected/extracted prior to clustering. Here we introduce sc-CGconv (copula based graph convolution network for single clustering), a stepwise robust unsupervised feature extraction and clustering approach that formulates and aggregates cell-cell relationships using copula correlation (Ccor), followed by a graph convolution network based clustering approach. sc-CGconv formulates a cell-cell graph using Ccor that is learned by a graph-based artificial intelligence model, graph convolution network. The learned representation (low dimensional embedding) is utilized for cell clustering. sc-CGconv features the following advantages. a. sc-CGconv works with substantially smaller sample sizes to identify homogeneous clusters. b. sc-CGconv can model the expression co-variability of a large number of genes, thereby outperforming state-of-the-art gene selection/extraction methods for clustering. c. sc-CGconv preserves the cell-to-cell variability within the selected gene set by constructing a cell-cell graph through copula correlation measure. d. sc-CGconv provides a topology-preserving embedding of cells in low dimensional space.

Consensus clustering of single-cell RNA-seq data by enhancing network affinity.

Elucidation of cell subpopulations at high resolution is a key and challenging goal of single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) data analysis. Although unsupervised clustering methods have been proposed for de novo identification of cell populations, their performance and robustness suffer from the high variability, low capture efficiency and high dropout rates which are characteristic of scRNA-seq experiments. Here, we present a novel unsupervised method for Single-cell Clustering by Enhancing Network Affinity (SCENA), which mainly employed three strategies: selecting multiple gene sets, enhancing local affinity among cells and clustering of consensus matrices. Large-scale validations on 13 real scRNA-seq datasets show that SCENA has high accuracy in detecting cell populations and is robust against dropout noise. When we applied SCENA to large-scale scRNA-seq data of mouse brain cells, known cell types were successfully detected, and novel cell types of interneurons were identified with differential expression of gamma-aminobutyric acid receptor subunits and transporters. SCENA is equipped with CPU + GPU (Central Processing Units + Graphics Processing Units) heterogeneous parallel computing to achieve high running speed. The high performance and running speed of SCENA combine into a new and efficient platform for biological discoveries in clustering analysis of large and diverse scRNA-seq datasets.

FEATS: feature selection-based clustering of single-cell RNA-seq data.

Advances in next-generation sequencing have made it possible to carry out transcriptomic studies at single-cell resolution and generate vast amounts of single-cell RNA sequencing (RNA-seq) data rapidly. Thus, tools to analyze this data need to evolve as well as to improve accuracy and efficiency. We present FEATS, a Python software package, that performs clustering on single-cell RNA-seq data. FEATS is capable of performing multiple tasks such as estimating the number of clusters, conducting outlier detection and integrating data from various experiments. We develop a univariate feature selection-based approach for clustering, which involves the selection of top informative features to improve clustering performance. This is motivated by the fact that cell types are often manually determined using the expression of only a few known marker genes. On a variety of single-cell RNA-seq datasets, FEATS gives superior performance compared with the current tools, in terms of adjusted Rand index and estimating the number of clusters. It achieves a 22% improvement in clustering and more accurately estimates the number of clusters when compared with other tools. In addition to cluster estimation, FEATS also performs outlier detection and data integration while giving an excellent computational performance. Thus, FEATS is a comprehensive clustering tool capable of addressing the challenges during the clustering of single-cell RNA-seq data. The installation instructions and documentation of FEATS is available at https://edwinv87.github.io/feats/. Supplementary data are available online at https://academic.oup.com/bib.

Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data

In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsupervised clustering problems and show its effectiveness in the field of single-cell RNA sequencing (scRNA-Seq). The goal of scRNA-Seq experiments is often the definition and cataloguing of cell types from the transcriptional output of individual cells. To improve the clustering of small disease- or tissue-specific datasets, for which the identification of rare cell types is often problematic, we propose a transfer learning method to utilize large and well-annotated reference datasets, such as those produced by the Human Cell Atlas. Our approach modifies the dataset of interest while incorporating key information from the larger reference dataset via Non-negative Matrix Factorization (NMF). The modified dataset is subsequently provided to a clustering algorithm. We empirically evaluate the benefits of our approach on simulated scRNA-Seq data as well as on publicly available datasets. Finally, we present results for the analysis of a recently published small dataset and find improved clustering when transferring knowledge from a large reference dataset. Implementations of the method are available at https://github.com/nicococo/scRNA.

Publisher Correction: Challenges in unsupervised clustering of single-cell RNA-seq data.

During typesetting of this article, errors were inadvertently introduced to the hyperlinked URLs of some of the clustering tools in table 1 (Seurat, CIDR, pcaReduce and mpath), as well as to the numbering of the bold-text annotations in the reference list. The article has now been corrected online. The editors apologize for this error.

A multitask clustering approach for single-cell RNA-seq analysis in Recessive Dystrophic Epidermolysis Bullosa.

Single-cell RNA sequencing (scRNA-seq) has been widely applied to discover new cell types by detecting sub-populations in a heterogeneous group of cells. Since scRNA-seq experiments have lower read coverage/tag counts and introduce more technical biases compared to bulk RNA-seq experiments, the limited number of sampled cells combined with the experimental biases and other dataset specific variations presents a challenge to cross-dataset analysis and discovery of relevant biological variations across multiple cell populations. In this paper, we introduce a method of variance-driven multitask clustering of single-cell RNA-seq data (scVDMC) that utilizes multiple single-cell populations from biological replicates or different samples. scVDMC clusters single cells in multiple scRNA-seq experiments of similar cell types and markers but varying expression patterns such that the scRNA-seq data are better integrated than typical pooled analyses which only increase the sample size. By controlling the variance among the cell clusters within each dataset and across all the datasets, scVDMC detects cell sub-populations in each individual experiment with shared cell-type markers but varying cluster centers among all the experiments. Applied to two real scRNA-seq datasets with several replicates and one large-scale droplet-based dataset on three patient samples, scVDMC more accurately detected cell populations and known cell markers than pooled clustering and other recently proposed scRNA-seq clustering methods. In the case study applied to in-house Recessive Dystrophic Epidermolysis Bullosa (RDEB) scRNA-seq data, scVDMC revealed several new cell types and unknown markers validated by flow cytometry. MATLAB/Octave code available at https://github.com/kuanglab/scVDMC.