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学者姓名：苏雅茹
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Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNAseq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios. © The Author(s) 2024.
Keyword ：
Benchmark Benchmark Gene set scoring Gene set scoring Pathway analysis Pathway analysis Single-cell ATAC-seq Single-cell ATAC-seq Single-cell RNA-seq Single-cell RNA-seq
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| GB/T 7714 | Wang, X. , Lian, Q. , Dong, H. et al. Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data [J]. | Genomics, Proteomics and Bioinformatics , 2024 , 22 (2) . |
| MLA | Wang, X. et al. "Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data" . | Genomics, Proteomics and Bioinformatics 22 . 2 (2024) . |
| APA | Wang, X. , Lian, Q. , Dong, H. , Xu, S. , Su, Y. , Wu, X. . Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data . | Genomics, Proteomics and Bioinformatics , 2024 , 22 (2) . |
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Abstract ：
Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.
Keyword ：
Benchmark Benchmark Gene set scoring Gene set scoring Pathway analysis Pathway analysis Single-cell ATAC-seq Single-cell ATAC-seq Single-cell RNA-seq Single-cell RNA-seq
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| GB/T 7714 | Wang, Xi , Lian, Qiwei , Dong, Haoyu et al. Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data [J]. | GENOMICS PROTEOMICS & BIOINFORMATICS , 2024 , 22 (2) . |
| MLA | Wang, Xi et al. "Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data" . | GENOMICS PROTEOMICS & BIOINFORMATICS 22 . 2 (2024) . |
| APA | Wang, Xi , Lian, Qiwei , Dong, Haoyu , Xu, Shuo , Su, Yaru , Wu, Xiaohui . Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data . | GENOMICS PROTEOMICS & BIOINFORMATICS , 2024 , 22 (2) . |
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Constructing tensor with low-rank prior is the crucial issue of tensor based multi-view subspace cluster-ing methods, but there are still some shortcomings. First, they cannot adaptively allocate the contribution of different views, resulting in the learned tensor being suboptimal. Second, they pursue low-rank tensor for different data through Discrete Fourier Transform based tensor nuclear norm, which lacks general-ity. To overcome these problems, we propose an invertible linear transforms based adaptive multi-view subspace clustering method, named ILTMSC. Firstly, we mine the potential low-order representation of each view through self-representation subspace learning. Then, we capture high-order representation by integrating low-order representations with adaptive weights into a tensor and then rotated. This strategy can integrate tensor adaptively and handle the noise effectively. Finally, we approximate the low-rank tensor with a recently proposed invertible linear transforms based tensor nuclear norm. Such a new ten-sor nuclear norm makes our model more general because it can use different invertible linear transforms for different tensor data. Moreover, an adaptive weighted tensor singular value thresholding operator is proposed for capturing the new tensor nuclear norm. Our model could be solved by convex optimization efficiently. Extensive experiments on multi-view datasets validate the effectiveness and robustness of our method.(c) 2023 Elsevier B.V. All rights reserved.
Keyword ：
Invertible linear transforms Invertible linear transforms Low-rank tensor Low-rank tensor Multi-view subspace clustering Multi-view subspace clustering Tensor nuclear norm Tensor nuclear norm
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| GB/T 7714 | Su, Yaru , Hong, Zhenning , Wu, Xiaohui et al. Invertible linear transforms based adaptive multi-view subspace clustering [J]. | SIGNAL PROCESSING , 2023 , 209 . |
| MLA | Su, Yaru et al. "Invertible linear transforms based adaptive multi-view subspace clustering" . | SIGNAL PROCESSING 209 (2023) . |
| APA | Su, Yaru , Hong, Zhenning , Wu, Xiaohui , Lu, Canyi . Invertible linear transforms based adaptive multi-view subspace clustering . | SIGNAL PROCESSING , 2023 , 209 . |
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为了探索非凸方法在多视图聚类方面的应用,基于非凸替换函数和子空间学习,提出非凸张量多视图子空间聚类算法.该算法不仅对多视图数据进行自表示学习来达到学习低维子空间的目的,而且采用带有旋转的张量结构对张量的高阶关联进行挖掘.同时,使用非凸函数替换和广义奇异值算子进行张量最小化问题的求解,从而实现对张量秩的近似.最后基于联合优化所得关联/仿射矩阵实现聚类操作,在不同类型的多视图数据集上的大量实验验证了该方法的聚类效果.
Keyword ：
多视图聚类 多视图聚类 子空间学习 子空间学习 张量约束 张量约束 非凸函数 非凸函数
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| GB/T 7714 | 洪振宁 , 苏雅茹 . 非凸张量多视图子空间聚类 [J]. | 福州大学学报（自然科学版） , 2022 , 50 (6) : 737-741 . |
| MLA | 洪振宁 et al. "非凸张量多视图子空间聚类" . | 福州大学学报（自然科学版） 50 . 6 (2022) : 737-741 . |
| APA | 洪振宁 , 苏雅茹 . 非凸张量多视图子空间聚类 . | 福州大学学报（自然科学版） , 2022 , 50 (6) , 737-741 . |
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Routing is a complex and critical stage in the physical design of Very Large Scale Integration (VLSI), minimizing interconnect length and delay to optimize overall chip performance. With the rapid development of modern technology, VLSI routing faces enormous challenges such as large delay, high congestion, and high-power consumption. As a rising optimization method, Swarm Intelligence (SI) inspired from collective intelligence behaviors through cooperation or interaction with the environment provides effectiveness and robustness for solving NP-hard problems. Many researchers have consequently used SI techniques to solve routing-related problems in VLSI. This paper reviews the application of several SI techniques to the VLSI routing filed. Firstly, five commonly used SI techniques and related models, and three classic routing problems are described: Steiner tree construction, global routing and detailed routing. Then an overview of the current state of this field is given according to the above categories, and the survey offers informative discussions from five aspects: 1) Steiner minimum tree construction; 2) wirelength-driven routing; 3) obstacle-avoiding routing; 4) timing-driven routing; 5) power-driven routing. Finally, under three new technology models: X-architecture, multiple dynamic supply voltage and via-pillar, the future development trends are pointed as follows: 1) suggesting suitable SI techniques to specific routing problems for advanced technology models; 2) exploring new and available SI techniques that have not yet been applied to VLSI routing.
Keyword ：
Particle swarm optimization Particle swarm optimization routing routing Steiner tree construction Steiner tree construction swarm intelligence swarm intelligence very large scale integration very large scale integration
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| GB/T 7714 | Chen, Xiaohua , Liu, Genggeng , Xiong, Naixue et al. A Survey of Swarm Intelligence Techniques in VLSI Routing Problems [J]. | IEEE ACCESS , 2020 , 8 : 26266-26292 . |
| MLA | Chen, Xiaohua et al. "A Survey of Swarm Intelligence Techniques in VLSI Routing Problems" . | IEEE ACCESS 8 (2020) : 26266-26292 . |
| APA | Chen, Xiaohua , Liu, Genggeng , Xiong, Naixue , Su, Yaru , Chen, Guolong . A Survey of Swarm Intelligence Techniques in VLSI Routing Problems . | IEEE ACCESS , 2020 , 8 , 26266-26292 . |
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Alternative polyadenylation (APA) has been implicated to play an important role in post-transcriptional regulation by regulating mRNA abundance, stability, localization and translation, which contributes considerably to transcriptome diversity and gene expression regulation. RNA-seq has become a routine approach for transcriptome profiling, generating unprecedented data that could be used to identify and quantify APA site usage. A number of computational approaches for identifying APA sites and/or dynamic APA events from RNA-seq data have emerged in the literature, which provide valuable yet preliminary results that should be refined to yield credible guidelines for the scientific community. In this review, we provided a comprehensive overview of the status of currently available computational approaches. We also conducted objective benchmarking analysis using RNA-seq data sets from different species (human, mouse and Arabidopsis) and simulated data sets to present a systematic evaluation of 11 representative methods. Our benchmarking study showed that the overall performance of all tools investigated is moderate, reflecting that there is still lot of scope to improve the prediction of APA site or dynamic APA events from RNA-seq data. Particularly, prediction results from individual tools differ considerably, and only a limited number of predicted APA sites or genes are common among different tools. Accordingly, we attempted to give some advice on how to assess the reliability of the obtained results. We also proposed practical recommendations on the appropriate method applicable to diverse scenarios and discussed implications and future directions relevant to profiling APA from RNA-seq data.
Keyword ：
3 ' untranslated region 3 ' untranslated region alternative polyadenylation alternative polyadenylation benchmark benchmark predictive modeling predictive modeling RNA-seq RNA-seq
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| GB/T 7714 | Chen, Moliang , Ji, Guoli , Fu, Hongjuan et al. A survey on identification and quantification of alternative polyadenylation sites from RNA-seq data [J]. | BRIEFINGS IN BIOINFORMATICS , 2020 , 21 (4) : 1261-1276 . |
| MLA | Chen, Moliang et al. "A survey on identification and quantification of alternative polyadenylation sites from RNA-seq data" . | BRIEFINGS IN BIOINFORMATICS 21 . 4 (2020) : 1261-1276 . |
| APA | Chen, Moliang , Ji, Guoli , Fu, Hongjuan , Lin, Qianmin , Ye, Congting , Ye, Wenbin et al. A survey on identification and quantification of alternative polyadenylation sites from RNA-seq data . | BRIEFINGS IN BIOINFORMATICS , 2020 , 21 (4) , 1261-1276 . |
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In the sparse vector recovery problem, the L-0-norm can be approximated by a convex function or a nonconvex function to achieve sparse solutions. In the low-rank matrix recovery problem, the nonconvex matrix rank can be replaced by a convex function or a nonconvex function on the singular value of matrix to achieve low-rank solutions. Although the convex relaxation can easily lead to the optimal solution, the nonconvex approximation tends to yield more sparse or lower rank local solutions. As a natural extension of vector and matrix to high order structure, tensor can better represent the essential structure of data for modeling the high-dimensional data. In this paper, we study the low tubal rank tensor recovery problem by nonconvex optimization. Instead of using convex tensor nuclear norm, we use nonconvex surrogate functions to approximate the tensor tubal rank, and propose a tensor based iteratively reweighted nuclear norm solver. We further provide the convergence analysis of our new solver. Sufficient experiments on synthetic data and real images verify the effectiveness of our new method.
Keyword ：
convergence analysis convergence analysis low tubal rank tensor recovery low tubal rank tensor recovery Nonconvex optimization Nonconvex optimization
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| GB/T 7714 | Su, Yaru , Wu, Xiaohui , Liu, Genggeng . Nonconvex Low Tubal Rank Tensor Minimization [J]. | IEEE ACCESS , 2019 , 7 : 170831-170843 . |
| MLA | Su, Yaru et al. "Nonconvex Low Tubal Rank Tensor Minimization" . | IEEE ACCESS 7 (2019) : 170831-170843 . |
| APA | Su, Yaru , Wu, Xiaohui , Liu, Genggeng . Nonconvex Low Tubal Rank Tensor Minimization . | IEEE ACCESS , 2019 , 7 , 170831-170843 . |
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BackgroundSingle-cell RNA-sequencing (scRNA-seq) is fast becoming a powerful tool for profiling genome-scale transcriptomes of individual cells and capturing transcriptome-wide cell-to-cell variability. However, scRNA-seq technologies suffer from high levels of technical noise and variability, hindering reliable quantification of lowly and moderately expressed genes. Since most downstream analyses on scRNA-seq, such as cell type clustering and differential expression analysis, rely on the gene-cell expression matrix, preprocessing of scRNA-seq data is a critical preliminary step in the analysis of scRNA-seq data.ResultsWe presented scNPF, an integrative scRNA-seq preprocessing framework assisted by network propagation and network fusion, for recovering gene expression loss, correcting gene expression measurements, and learning similarities between cells. scNPF leverages the context-specific topology inherent in the given data and the priori knowledge derived from publicly available molecular gene-gene interaction networks to augment gene-gene relationships in a data driven manner. We have demonstrated the great potential of scNPF in scRNA-seq preprocessing for accurately recovering gene expression values and learning cell similarity networks. Comprehensive evaluation of scNPF across a wide spectrum of scRNA-seq data sets showed that scNPF achieved comparable or higher performance than the competing approaches according to various metrics of internal validation and clustering accuracy. We have made scNPF an easy-to-use R package, which can be used as a versatile preprocessing plug-in for most existing scRNA-seq analysis pipelines or tools.ConclusionsscNPF is a universal tool for preprocessing of scRNA-seq data, which jointly incorporates the global topology of priori interaction networks and the context-specific information encapsulated in the scRNA-seq data to capture both shared and complementary knowledge from diverse data sources. scNPF could be used to recover gene signatures and learn cell-to-cell similarities from emerging scRNA-seq data to facilitate downstream analyses such as dimension reduction, cell type clustering, and visualization.
Keyword ：
Cell type clustering Cell type clustering Dropout imputation Dropout imputation Network propagation Network propagation Similarity measurement Similarity measurement Single cell RNA-sequencing Single cell RNA-sequencing
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| GB/T 7714 | Ye, Wenbin , Ji, Guoli , Ye, Pengchao et al. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data [J]. | BMC GENOMICS , 2019 , 20 . |
| MLA | Ye, Wenbin et al. "scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data" . | BMC GENOMICS 20 (2019) . |
| APA | Ye, Wenbin , Ji, Guoli , Ye, Pengchao , Long, Yuqi , Xiao, Xuesong , Li, Shuchao et al. scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data . | BMC GENOMICS , 2019 , 20 . |
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In this paper, we study the problem of low-rank tensor completion with the purpose of recovering a low-rank tensor from a tensor with partial observed items. To date, there are several different definitions of tensor ranks. We focus the study on the low tubal rank tensor completion task. Previous works solve the low tubal rank tensor completion/recovery problems by convex tensor nuclear norm minimization. However, this kind of tensor nuclear norm is orientation dependent, which is originally due to the definition of tensor-tensor product. Based on the convex tensor nuclear norm minimization, the tensor recovery performance varies when the orientation of the input data is different. However, in practice, it is generally hard to choose the best way of the data input. To address this issue, we propose a new convex model which is based on the sum of tensor nuclear norm minimization. It includes the existing tensor nuclear norm minimization model as a special case which is corresponding to an orientation of the input data. The proposed model is convex and thus can be solved efficiently. Numerical experiments on images and video sequences demonstrate the effectiveness of our proposed method.
Keyword ：
convex optimization convex optimization sum of tensor nuclear norm sum of tensor nuclear norm Tensor completion Tensor completion tensor nuclear norm tensor nuclear norm tensor SVD tensor SVD tensor-tensor product tensor-tensor product
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| GB/T 7714 | Su, Yaru , Wu, Xiaohui , Liu, Wenxi . Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization [J]. | IEEE ACCESS , 2019 , 7 : 134943-134953 . |
| MLA | Su, Yaru et al. "Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization" . | IEEE ACCESS 7 (2019) : 134943-134953 . |
| APA | Su, Yaru , Wu, Xiaohui , Liu, Wenxi . Low-Rank Tensor Completion by Sum of Tensor Nuclear Norm Minimization . | IEEE ACCESS , 2019 , 7 , 134943-134953 . |
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BackgroundAlternative polyadenylation (APA) has emerged as a pervasive mechanism that contributes to the transcriptome complexity and dynamics of gene regulation. The current tsunami of whole genome poly(A) site data from various conditions generated by 3 end sequencing provides a valuable data source for the study of APA-related gene expression. Cluster analysis is a powerful technique for investigating the association structure among genes, however, conventional gene clustering methods are not suitable for APA-related data as they fail to consider the information of poly(A) sites (e.g., location, abundance, number, etc.) within each gene or measure the association among poly(A) sites between two genes.ResultsHere we proposed a computational framework, named PASCCA, for clustering genes from replicated or unreplicated poly(A) site data using canonical correlation analysis (CCA). PASCCA incorporates multiple layers of gene expression data from both the poly(A) site level and gene level and takes into account the number of replicates and the variability within each experimental group. Moreover, PASCCA characterizes poly(A) sites in various ways including the abundance and relative usage, which can exploit the advantages of 3 end deep sequencing in quantifying APA sites. Using both real and synthetic poly(A) site data sets, the cluster analysis demonstrates that PASCCA outperforms other widely-used distance measures under five performance metrics including connectivity, the Dunn index, average distance, average distance between means, and the biological homogeneity index. We also used PASCCA to infer APA-specific gene modules from recently published poly(A) site data of rice and discovered some distinct functional gene modules. We have made PASCCA an easy-to-use R package for APA-related gene expression analyses, including the characterization of poly(A) sites, quantification of association between genes, and clustering of genes.ConclusionsBy providing a better treatment of the noise inherent in repeated measurements and taking into account multiple layers of poly(A) site data, PASCCA could be a general tool for clustering and analyzing APA-specific gene expression data. PASCCA could be used to elucidate the dynamic interplay of genes and their APA sites among various biological conditions from emerging 3 end sequencing data to address the complex biological phenomenon.
Keyword ：
Alternative polyadenylation Alternative polyadenylation Canonical correlation analysis Canonical correlation analysis Cluster analysis Cluster analysis Gene expression Gene expression Network inference Network inference
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| GB/T 7714 | Ye, Wenbin , Long, Yuqi , Ji, Guoli et al. Cluster analysis of replicated alternative polyadenylation data using canonical correlation analysis [J]. | BMC GENOMICS , 2019 , 20 . |
| MLA | Ye, Wenbin et al. "Cluster analysis of replicated alternative polyadenylation data using canonical correlation analysis" . | BMC GENOMICS 20 (2019) . |
| APA | Ye, Wenbin , Long, Yuqi , Ji, Guoli , Su, Yaru , Ye, Pengchao , Fu, Hongjuan et al. Cluster analysis of replicated alternative polyadenylation data using canonical correlation analysis . | BMC GENOMICS , 2019 , 20 . |
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