SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement

Liang, Zhenlan; Li, Min; Zheng, Ruiqing; Tian, Yu; Yan, Xuhua; Chen, Jin; Wu, Fang‐Xiang

doi:10.1016/j.gpb.2020.09.004

Cited by 26 publications

(9 citation statements)

References 60 publications

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“…Adjusted rand index (ARI) and normalized mutual information (NMI) are employed to evaluate the performance of batch correction methods on preserving cellular heterogeneity. NMI and ARI compare the overlap between clustering results and annotated labels ( Liang et al , 2021 ; Tian et al , 2021 ). Higher NMI and ARI indicate better match between clustering results and annotations.…”

Section: Resultsmentioning

confidence: 99%

CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity

Yan

Zheng

et al. 2023

Bioinformatics

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Motivation Integration of growing single-cell RNA sequencing datasets helps better understand cellular identity and function. The major challenge for integration is removing batch effects while preserving biological heterogeneities. Advances in contrastive learning have inspired several contrastive learning-based batch correction methods. However, existing contrastive-learning based methods exhibit noticeable ad hoc trade-off between batch mixing and preservation of cellular heterogeneities (mix-heterogeneity trade-off). Therefore, a deliberate mix-heterogeneity trade-off is expected to yield considerable improvements in scRNA-seq dataset integration. Results We develop a novel contrastive learning-based batch correction framework, CIAIRE, which achieves superior mix-heterogeneity trade-off. The key contributions of CLAIRE are proposal of two complementary strategies: construction strategy and refinement strategy, to improve the appropriateness of positive pairs. Construction strategy dynamically generates positive pairs by augmenting inter-batch mutual nearest neighbors (MNN) with intra-batch k-nearest neighbors (KNN), which improves the coverage of positive pairs for the whole distribution of shared cell types between batches. Refinement strategy aims to automatically reduce the potential false positive pairs from the construction strategy, which resorts to the memory effect of deep neural networks. We demonstrate that CLAIRE possesses superior mix-heterogeneity trade-off over existing contrastive learning-based methods. Benchmark results on six real datasets also show that CLAIRE achieves the best integration performance against eight state-of-the-art methods. Finally, comprehensive experiments are conducted to validate the effectiveness of CLAIRE. Availability https://github.com/CSUBioGroup/CLAIRE-release Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity

Yan

Zheng

et al. 2023

Bioinformatics

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“…Where ∥ C ∥ 1 can also be replaced by ∥ C ∥ 2 . Based on the SEM scheme, there have been a variety of works [ 31 – 33 ]. However, it is not necessarily guaranteed that the hand-crafted feature exploit robust descriptions of data samples.…”

Section: Methodsmentioning

confidence: 99%

“…Subspace clustering aims to reveal the internal structure of complex data in an unsupervised way [ 31 ]. Recently, some new single cell analysis methods based-subspace clustering have been proposed, including ENCORE [ 32 ] and SSRE [ 33 ]. ENCORE can distinguish informative features from noise based on feature density profiles, such strategy called as “entropy subspace” separation.…”

Section: Introductionmentioning

confidence: 99%

scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data

et al. 2022

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Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells. However, single cell study still remains great challenges due to its high noise and dimension. Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. In this paper, we propose a deep sparse subspace clustering method scDSSC combining noise reduction and dimensionality reduction for scRNA-seq data, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Experiments on a variety of scRNA-seq datasets from thousands to tens of thousands of cells have shown that scDSSC can significantly improve clustering performance and facilitate the interpretability of clustering and downstream analysis. Compared to some popular scRNA-deq analysis methods, scDSSC outperformed state-of-the-art methods under various clustering performance metrics.

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“…SAME-clustering [26] combined a maximally diverse subset of four clustering solutions obtained from five individual clustering methods, then the subset was combined with the expectation-maximization (EM) algorithm to build an ensemble clustering solution. Among all these methods, we find that hierarchical clustering [10,15,16,18,25,[27][28][29] and graph-based clustering [30][31][32][33][34] such as spectral clustering and Louvain community detection algorithm are the most popular approaches in the downstream clustering analysis [9,12,[21][22][23][24]35] . Additionally, densitybased clustering is also widely used in scRNA-seq data analysis for the identification of outlier cells [36,37] .…”

Section: Introductionmentioning

confidence: 99%

A data-driven clustering recommendation method for single-cell RNA-sequencing data

Tian

Zheng

Liang

et al. 2021

Tsinghua Sci. Technol.

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Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) technology makes it possible to solve biological problems at the single-cell resolution. One of the critical steps in cellular heterogeneity analysis is the cell type identification. Diverse scRNA-seq clustering methods have been proposed to partition cells into clusters. Among all the methods, hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning, dropout imputation, and dimensionality reduction. In this study, we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological conditions. The analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension, while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in two-dimension. Motivated by this finding, a new strategy, called QRS, is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or not. Finally, a data-driven clustering recommendation method, called DDCR, is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data. We perform DDCR on two typical single cell clustering methods, SC3 and RAFSIL, and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.

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SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement

Cited by 26 publications

References 60 publications

CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity

CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity

scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data

A data-driven clustering recommendation method for single-cell RNA-sequencing data

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