Single-cell RNA-seq clustering: datasets, models, and algorithms.
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ABSTRACT:
SUBMITTER: Peng L
PROVIDER: S-EPMC7549635 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Tumors are complex biological entities that comprise cell types of different origins, with different mutational profiles and different patterns of transcriptional dysregulation. The exploration of data related to cancer biology requires careful analytical methods to reflect the heterogeneity of cell populations in cancer samples. Single-cell techniques are now able to capture the transcriptional profiles of individual cells. However, the complexity of RNA-seq data, especially in cancer samples, makes it challenging to cluster single-cell profiles into groups that reflect the underlying cell types. We have developed a framework for a systematic examination of single-cell RNA-seq clustering algorithms for cancer data, which uses a range of well-established metrics to generate a unified quality score and algorithm ranking. To demonstrate this framework, we examined clustering performance of 15 different single-cell RNA-seq clustering algorithms on eight different cancer datasets. Our results suggest that the single-cell RNA-seq clustering algorithms fall into distinct groups by performance, with the highest clustering quality on non-malignant cells achieved by three algorithms: Seurat, bigSCale and Cell Ranger. However, for malignant cells, two additional algorithms often reach a better performance, namely Monocle and SC3. Their ability to detect known rare cell types was also among the best, along with Seurat. Our approach and results can be used by a broad audience of practitioners who analyze single-cell transcriptomic data in cancer research.
| S-EPMC9677128 | biostudies-literature
Project description:BACKGROUND:With the recent proliferation of single-cell RNA-Seq experiments, several methods have been developed for unsupervised analysis of the resulting datasets. These methods often rely on unintuitive hyperparameters and do not explicitly address the subjectivity associated with clustering. RESULTS:In this work, we present DendroSplit, an interpretable framework for analyzing single-cell RNA-Seq datasets that addresses both the clustering interpretability and clustering subjectivity issues. DendroSplit offers a novel perspective on the single-cell RNA-Seq clustering problem motivated by the definition of "cell type", allowing us to cluster using feature selection to uncover multiple levels of biologically meaningful populations in the data. We analyze several landmark single-cell datasets, demonstrating both the method's efficacy and computational efficiency. CONCLUSION:DendroSplit offers a clustering framework that is comparable to existing methods in terms of accuracy and speed but is novel in its emphasis on interpretabilty. We provide the full DendroSplit software package at https://github.com/jessemzhang/dendrosplit .
| S-EPMC5845381 | biostudies-literature
Project description:Clustering of cells based on gene expression is one of the major steps in single-cell RNA-sequencing (scRNA-seq) data analysis. One key challenge in cluster analysis is the unknown number of clusters and, for this issue, there is still no comprehensive solution. To enhance the process of defining meaningful cluster resolution, we compare Bayesian latent Dirichlet allocation (LDA) method to its non-parametric counterpart, hierarchical Dirichlet process (HDP) in the context of clustering scRNA-seq data. A potential main advantage of HDP is that it does not require the number of clusters as an input parameter from the user. While LDA has been used in single-cell data analysis, it has not been compared in detail with HDP. Here, we compare the cell clustering performance of LDA and HDP using four scRNA-seq datasets (immune cells, kidney, pancreas and decidua/placenta), with a specific focus on cluster numbers. Using both intrinsic (DB-index) and extrinsic (ARI) cluster quality measures, we show that the performance of LDA and HDP is dataset dependent. We describe a case where HDP produced a more appropriate clustering compared to the best performer from a series of LDA clusterings with different numbers of clusters. However, we also observed cases where the best performing LDA cluster numbers appropriately capture the main biological features while HDP tended to inflate the number of clusters. Overall, our study highlights the importance of carefully assessing the number of clusters when analyzing scRNA-seq data.
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Project description:Single-cell RNA sequencing has led to unprecedented levels of data complexity. Although several computational platforms are available, performing data analyses for multiple datasets remains a significant challenge. Here, we provide a comprehensive analytical protocol to interrogate multiple datasets on SingCellaR, an analysis package in R. This tool can be applied to general single-cell transcriptome analyses. We demonstrate steps for data analyses and visualization using bespoke pipelines, in conjunction with existing analysis tools to study human hematopoietic stem and progenitor cells. For complete details on the use and execution of this protocol, please refer to Roy et al. (2021).
| S-EPMC8980964 | biostudies-literature
Project description:MotivationAccurately clustering cell types from a mass of heterogeneous cells is a crucial first step for the analysis of single-cell RNA-seq (scRNA-Seq) data. Although several methods have been recently developed, they utilize different characteristics of data and yield varying results in terms of both the number of clusters and actual cluster assignments.ResultsHere, we present SAFE-clustering, single-cell aggregated (From Ensemble) clustering, a flexible, accurate and robust method for clustering scRNA-Seq data. SAFE-clustering takes as input, results from multiple clustering methods, to build one consensus solution. SAFE-clustering currently embeds four state-of-the-art methods, SC3, CIDR, Seurat and t-SNE + k-means; and ensembles solutions from these four methods using three hypergraph-based partitioning algorithms. Extensive assessment across 12 datasets with the number of clusters ranging from 3 to 14, and the number of single cells ranging from 49 to 32, 695 showcases the advantages of SAFE-clustering in terms of both cluster number (18.2-58.1% reduction in absolute deviation to the truth) and cluster assignment (on average 36.0% improvement, and up to 18.5% over the best of the four methods, measured by adjusted rand index). Moreover, SAFE-clustering is computationally efficient to accommodate large datasets, taking <10 min to process 28 733 cells.Availability and implementationSAFEclustering, including source codes and tutorial, is freely available at https://github.com/yycunc/SAFEclustering.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6477982 | biostudies-literature
Project description:Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as 'features'), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have a significant impact on the clustering accuracy. All existing scRNA-seq clustering tools include a feature selection step relying on some simple unsupervised feature selection methods, mostly based on the statistical moments of gene-wise expression distributions. In this work, we carefully evaluate the impact of feature selection on cell clustering accuracy. In addition, we develop a feature selection algorithm named FEAture SelecTion (FEAST), which provides more representative features. We apply the method on 12 public scRNA-seq datasets and demonstrate that using features selected by FEAST with existing clustering tools significantly improve the clustering accuracy.
| S-EPMC8644062 | biostudies-literature
Project description:Single-cell RNA-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/SC3). We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.
| S-EPMC5410170 | biostudies-literature
Project description:Integration of single-cell RNA-sequencing (scRNA-seq) datasets has become a standard part of the analysis, with conditional variational autoencoders (cVAE) being among the most popular approaches. Increasingly, researchers are asking to map cells across challenging cases such as cross-organs, species, or organoids and primary tissue, as well as different scRNA-seq protocols, including single-cell and single-nuclei. Current computational methods struggle to harmonize datasets with such substantial differences, driven by technical or biological variation. Here, we propose to address these challenges for the popular cVAE-based approaches by introducing and comparing a series of regularization constraints. The two commonly used strategies for increasing batch correction in cVAEs, that is Kullback-Leibler divergence (KL) regularization strength tuning and adversarial learning, suffer from substantial loss of biological information. Therefore, we adapt, implement, and assess alternative regularization strategies for cVAEs and investigate how they improve batch effect removal or better preserve biological variation, enabling us to propose an optimal cVAE-based integration strategy for complex systems. We show that using a VampPrior instead of the commonly used Gaussian prior not only improves the preservation of biological variation but also unexpectedly batch correction. Moreover, we show that our implementation of cycle-consistency loss leads to significantly better biological preservation than adversarial learning implemented in the previously proposed GLUE model. Additionally, we do not recommend relying only on the KL regularization strength tuning for increasing batch correction, as it removes both biological and batch information without discriminating between the two. Based on our findings, we propose a new model that combines VampPrior and cycle-consistency loss. We show that using it for datasets with substantial batch effects improves downstream interpretation of cell states and biological conditions. To ease the use of the newly proposed model, we make it available in the scvi-tools package as an external model named sysVI. Moreover, in the future, these regularization techniques could be added to other established cVAE-based models to improve the integration of datasets with substantial batch effects.
| S-EPMC10635119 | biostudies-literature
Project description:Single-cell computational pipelines involve two critical steps: organizing cells (clustering) and identifying the markers driving this organization (differential expression analysis). State-of-the-art pipelines perform differential analysis after clustering on the same dataset. We observe that because clustering "forces" separation, reusing the same dataset generates artificially low p values and hence false discoveries. We introduce a valid post-clustering differential analysis framework, which corrects for this problem. We provide software at https://github.com/jessemzhang/tn_test.
| S-EPMC7202736 | biostudies-literature
Project description:MotivationSingle-cell RNA sequencing has emerged as a powerful technology for studying gene expression at the individual cell level. Clustering individual cells into distinct subpopulations is fundamental in scRNA-seq data analysis, facilitating the identification of cell types and exploration of cellular heterogeneity. Despite the recent development of many deep learning-based single-cell clustering methods, few have effectively exploited the correlations among genes, resulting in suboptimal clustering outcomes.ResultsHere, we propose a novel masked autoencoder-based method, scMAE, for cell clustering. scMAE perturbs gene expression and employs a masked autoencoder to reconstruct the original data, learning robust and informative cell representations. The masked autoencoder introduces a masking predictor, which captures relationships among genes by predicting whether gene expression values are masked. By integrating this masking mechanism, scMAE effectively captures latent structures and dependencies in the data, enhancing clustering performance. We conducted extensive comparative experiments using various clustering evaluation metrics on 15 scRNA-seq datasets from different sequencing platforms. Experimental results indicate that scMAE outperforms other state-of-the-art methods on these datasets. In addition, scMAE accurately identifies rare cell types, which are challenging to detect due to their low abundance. Furthermore, biological analyses confirm the biological significance of the identified cell subpopulations.Availability and implementationThe source code of scMAE is available at: https://zenodo.org/records/10465991.
| S-EPMC10832357 | biostudies-literature