CIF: Small: Multiview Graph Learning with Applications to Single Cell Gene Expression Networks

CIF：小型：多视图图学习及其在单细胞基因表达网络中的应用

• 批准号: 2211645
• 负责人: Selin Aviyente
• 金额: $ 59.31万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211645&HistoricalAwards=false
• 关键词: CIF; Small; Multiview; Graph; Learning

Modern data analysis involves large sets of structured data, where the structure carries critical information about the nature of the data. The relationships between entities, such as features or data samples, are usually described by a graph structure. While many real-world data are intrinsically graph-structured, e.g. social and traffic networks, there are still a large number of applications where the graph topology is not readily available. For instance, gene regulations in biological applications or neuronal connections in the brain are not usually observed. Inferring the underlying structure is an essential task for such data. Most of the existing work on graph learning focuses on learning a single graph structure, assuming that the relations between the observed data samples are homogeneous. However, in many real-world applications, there are different forms of interactions between data samples, such as single-cell RNA sequencing (scRNA-seq) across multiple cell types. This project aims to address the multi-view graph-learning problem for heterogeneous data with a focus on gene regulatory network (GRN) inference from scRNA-seq.This project will introduce a multi-view framework to learn graphical structures from heterogeneous data. First, a new approach for learning signed graphs will be introduced. Signed graphs are commonly encountered in biological networks, where the positive and negative edges correspond to activating and inhibitory relationships, respectively. This framework will take the nonlinear nature of interactions between nodes into account through graph signal kernels. Second, a comprehensive framework for multi-view graph learning in two settings will be considered: i) multiple views of the same data and ii) heterogeneous data with unknown cluster information. In the first case, a joint learning approach where both individual graphs and a consensus graph are learned will be developed. In the second case, a unified framework that merges classical spectral clustering with graph signal smoothness will be developed for joint clustering and multi-view graph learning. The graph-learning algorithms will address some of the challenges encountered in gene regulatory network inference, such as non-Gaussian, nonlinear nature of gene expression data, changes in gene expression due to cell-cycle heterogeneity, and high sparsity due to low amounts of mRNA in individual cells. This project will provide interdisciplinary training to a diverse population of students at all levels. The research outcomes will be incorporated into a new online graduate course as part of the growing interest in data science. Finally, the proposed research will be incorporated into outreach efforts targeting K-12 female students and disseminated to the broader research community through publications, workshops and code sharing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

现代数据分析涉及大量结构化数据，其中结构带有有关数据性质的关键信息。图形结构通常描述实体之间的关系，例如特征或数据样本。虽然许多现实世界数据是本质上的图形结构的，例如社交和交通网络，仍然有许多应用程序不容易获得图形拓扑。例如，通常不会观察到生物应用或神经元连接中的基因调节。推断基础结构是此类数据的重要任务。假设观察到的数据样本之间的关系是同质的，那么图形学习上的大多数现有工作都集中在学习单个图结构上。但是，在许多实际应用中，数据样本之间存在不同形式的相互作用，例如多种细胞类型的单细胞RNA测序（SCRNA-SEQ）。该项目旨在解决异质数据的多视图图形学习问题，重点是SCRNA-SEQ的基因调节网络（GRN）推论。此项目将引入一个多视图框架，以从异质数据中学习图形结构。首先，将引入一种新的学习签名图的方法。签名的图通常在生物网络中遇到，在生物网络中，正和负边缘分别对应于激活和抑制关系。该框架将通过图形信号内核来考虑节点之间相互作用的非线性性质。其次，将考虑在两个设置中进行多视图图学习的综合框架：i）相同数据的多个视图和ii）具有未知群集信息的异质数据。在第一种情况下，将开发出一种共同学习方法，其中都将开发单个图形和共识图。在第二种情况下，将开发一个将经典光谱聚类与图形信号平滑度合并的统一框架将开发用于关节聚类和多视图图学习。图形学习算法将解决基因调节网络推断中遇到的一些挑战，例如非高斯，基因表达数据的非线性性质，基因表达的变化，细胞周期异质性引起的基因表达以及由于单个细胞中mRNA量低而引起的高稀疏性。该项目将向各个级别的学生提供跨学科的培训。研究结果将被纳入新的在线研究生课程中，这是对数据科学不断增长的兴趣的一部分。最后，拟议的研究将纳入针对K-12女学生的推广工作中，并通过出版物，研讨会和代码共享将更广泛的研究社区传播到更广泛的研究社区。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响来通过评估来获得支持的。

项目成果

Multiple Signed Graph Learning for Gene Regulatory Network Inference

• DOI: 10.1109/icassp49357.2023.10096490
• 发表时间: 2023-06
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Abdullah Karaaslanli;Satabdi Saha;T. Maiti;Selin Aviyente

Dynamic Signed Graph Learning

• DOI: 10.1109/icassp49357.2023.10094914
• 发表时间: 2023-06
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Abdullah Karaaslanli;Selin Aviyente

scSGL: kernelized signed graph learning for single-cell gene regulatory network inference

• DOI: 10.1093/bioinformatics/btac288
• 发表时间: 2022-05-06
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Karaaslanli, Abdullah;Saha, Satabdi;Maiti, Tapabrata
• 通讯作者: Maiti, Tapabrata