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学者姓名：苏雅茹
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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 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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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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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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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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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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为了更好地获取高维数据的特征,提出一种特征提取算法——低秩判别映射.首先基于低秩表示构造代表样本关联性的关联矩阵,然后利用关联矩阵应用判别准则.低秩表示以样本作为基函数,利用所有样本构建关联矩阵,其构造特点决定了关联矩阵能够很好地体现样本集的全局结构和样本之间的判别关系.人脸数据集的实验表明,低秩判别映射优于其他广泛应用的特征提取方法.
Keyword ：
低秩表示 低秩表示 判别准则 判别准则 特征提取 特征提取
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| GB/T 7714 | 苏雅茹 , 许智杰 , 吴小惠 . 基于低秩表示的判别特征提取算法 [J]. | 福州大学学报（自然科学版） , 2019 , 47 (1) : 12-17 . |
| MLA | 苏雅茹 et al. "基于低秩表示的判别特征提取算法" . | 福州大学学报（自然科学版） 47 . 1 (2019) : 12-17 . |
| APA | 苏雅茹 , 许智杰 , 吴小惠 . 基于低秩表示的判别特征提取算法 . | 福州大学学报（自然科学版） , 2019 , 47 (1) , 12-17 . |
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在低秩矩阵、张量最小化问题中,凸函数容易求得最优解,而非凸函数可以得到更低秩的局部解.文中基于非凸替换函数的低秩张量恢复问题,提出基于lp范数的非凸张量模型.采用迭代加权核范数算法求解模型,实现低秩张量最小化.在合成数据和真实图像上的大量实验验证文中方法的恢复性能.
Keyword ：
IRNN) IRNN) lp lp 低秩张量恢复,非凸惩罚函数, 低秩张量恢复,非凸惩罚函数, 范数,迭代加权核范数算法( 范数,迭代加权核范数算法(
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| GB/T 7714 | 苏雅茹 , 刘耿耿 , 刘文犀 et al. 基于lP范数的非凸低秩张量最小化 [J]. | 模式识别与人工智能 , 2019 , 32 (6) : 494-503 . |
| MLA | 苏雅茹 et al. "基于lP范数的非凸低秩张量最小化" . | 模式识别与人工智能 32 . 6 (2019) : 494-503 . |
| APA | 苏雅茹 , 刘耿耿 , 刘文犀 , 朱丹红 . 基于lP范数的非凸低秩张量最小化 . | 模式识别与人工智能 , 2019 , 32 (6) , 494-503 . |
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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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Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of similar to 0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap. Supplementary information Supplementary data are available at Bioinformatics online.
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| GB/T 7714 | Ji, Guoli , Ye, Wenbin , Su, Yaru et al. AStrap: identification of alternative splicing from transcript sequences without a reference genome [J]. | BIOINFORMATICS , 2019 , 35 (15) : 2654-2656 . |
| MLA | Ji, Guoli et al. "AStrap: identification of alternative splicing from transcript sequences without a reference genome" . | BIOINFORMATICS 35 . 15 (2019) : 2654-2656 . |
| APA | Ji, Guoli , Ye, Wenbin , Su, Yaru , Chen, Moliang , Huang, Guangzao , Wu, Xiaohui . AStrap: identification of alternative splicing from transcript sequences without a reference genome . | BIOINFORMATICS , 2019 , 35 (15) , 2654-2656 . |
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