Single-Cell Transcriptome Data Clustering via Multinomial Modeling and Adaptive Fuzzy K-Means Algorithm

Chen, Liang; Wang, Weinan; Zhai, Yuyao; Deng, Minghua

doi:10.3389/fgene.2020.00295

Cited by 16 publications

(8 citation statements)

References 52 publications

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“…We take the classification loss function as the standard cross-entropy function, which only operates on the well-labeled source dataset. (6) This step actually also ensures that the encoder can map cells in the target dataset that are close to the expression pattern of known cell types into the vicinity of the corresponding cell type location.…”

Section: Supervised Classification On Well-labeled Source Datasetmentioning

confidence: 99%

“…For example, exploring cell-to-cell interactions and gene-to-gene interactions are often based on specific cell types [4,5]. In the past few years, a large volume of single-cell transcriptome unsupervised clustering algorithms have emerged based on the similarity of gene expression patterns [6][7][8][9]. However, in the absence of a unified standard, the clustering results of different algorithms usually show a little degree of overlap and even vary widely [10].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we combine deep learning technique with statistical modeling and propose an end-to-end single-cell RNA-seq data supervised clustering and annotation framework, named scAnCluster, based on our previous unsupervised clustering works scDMFK [6] and scziDesk [7]. The three methods adhere to similar modeling concepts, namely simultaneous denoising, dimensionality reduction and clustering.…”

Section: Introductionmentioning

confidence: 99%

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Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation

Chen

Zhai

et al. 2020

Genes

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As single-cell RNA sequencing technologies mature, massive gene expression profiles can be obtained. Consequently, cell clustering and annotation become two crucial and fundamental procedures affecting other specific downstream analyses. Most existing single-cell RNA-seq (scRNA-seq) data clustering algorithms do not take into account the available cell annotation results on the same tissues or organisms from other laboratories. Nonetheless, such data could assist and guide the clustering process on the target dataset. Identifying marker genes through differential expression analysis to manually annotate large amounts of cells also costs labor and resources. Therefore, in this paper, we propose a novel end-to-end cell supervised clustering and annotation framework called scAnCluster, which fully utilizes the cell type labels available from reference data to facilitate the cell clustering and annotation on the unlabeled target data. Our algorithm integrates deep supervised learning, self-supervised learning and unsupervised learning techniques together, and it outperforms other customized scRNA-seq supervised clustering methods in both simulation and real data. It is particularly worth noting that our method performs well on the challenging task of discovering novel cell types that are absent in the reference data.

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Section: Supervised Classification On Well-labeled Source Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation

Chen

Zhai

et al. 2020

Genes

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“…Typically, the clustering algorithms take low-dimensional representations of cells as input, instead of raw gene expression profiles. In the deep learning setting [23] , [24] , [25] , [113] , [114] , [115] , [116] , [117] , [118] , [119] , [120] , [121] , [122] , the two steps, representation learning and clustering, can be done sequentially or simultaneously.…”

Section: Applications Of Deep Learning In Scrna-seq Data Analysismentioning

confidence: 99%

Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review

Brendel

Bai

et al. 2022

Genomics, Proteomics &Amp; Bioinformatics

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Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data to improve downstream analysis. The present review aims at surveying recently developed deep learning techniques in scRNA-seq data analysis, identifying key steps within the scRNA-seq data analysis pipeline that have been advanced by deep learning, and explaining the benefits of deep learning over more conventional analytic tools. Finally, we summarize the challenges in current deep learning approaches faced within scRNA-seq data and discuss potential directions for improvements in deep learning algorithms for scRNA-seq data analysis.

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“…al [19] developed a unifying tool based on the autoencoders to facilitate analyzing the single-cell RNA-seq data. Chen et al [20] designed an adaptive fuzzy K-means algorithm combined with the deep autoencoder technique.…”

mentioning

confidence: 99%

Multiobjective Deep Clustering and its Applications in Single-cell RNA-seq Data

Wang

Bian

Wong

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

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Single-cell RNA sequencing is a transformative technology that enables us to study the heterogeneity of the tissue at the cellular level. Clustering is used as the key computational approach to group cells under the transcriptome profiles from single-cell RNA-seq data. However, accurate identification of distinct cell types is facing the challenge of high-dimensionality, and it could cause uninformative clusters when clustering is directly applied on the original transcriptome. To address such challenge, an evolutionary multiobjective deep clustering algorithm (EMDC) is proposed to identify single-cell RNA-seq data in this study. First, EMDC removes redundant and irrelevant genes by applying the differential gene expression analysis to identify differentially expressed genes across biological conditions. After that, deep autoencoder is proposed to project the high-dimensional data into different low-dimensional nonlinear embedding subspaces under different bottleneck layers. Then, the basic clustering algorithm is applied in those nonlinear embedding subspaces to generate some basic clustering results to produce the cluster ensemble. To lessen the unnecessary cost produced by those clusterings in the ensemble, the multiobjective evolutionary optimization is designed to prune the basic clustering results in the ensemble, unleashing its cell type discovery performance under three objective functions. Multiple experiments have been conducted on thirty synthetic singlecell RNA-seq datasets and six real single-cell RNA-seq datasets, which reveal that EMDC outperforms eight other clustering methods and three multiobjective optimization algorithms in cell type identification. In addition, we have also conducted extensive comparisons to effectively demonstrate the impact of each component in our proposed EMDC.

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Single-Cell Transcriptome Data Clustering via Multinomial Modeling and Adaptive Fuzzy K-Means Algorithm

Cited by 16 publications

References 52 publications

Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation

Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation

Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review

Multiobjective Deep Clustering and its Applications in Single-cell RNA-seq Data

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