Nonparanormal Graph Quilting with Applications to Calcium Imaging

• URL: http://arxiv.org/abs/2305.13491v1
• Date: Mon, 22 May 2023 21:16:01 GMT
• Title: Nonparanormal Graph Quilting with Applications to Calcium Imaging
• Authors: Andersen Chang and Lili Zheng and Gautam Dasarthy and Genevera I. Allen
• Abstract summary: We study two approaches for nonparanormal Graph Quilting based on the Gaussian copula graphical model. Our approaches yield more scientifically meaningful functional connectivity estimates compared to existing Gaussian graph quilting methods for this calcium imaging data set.
• Score: 1.1470070927586016
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Probabilistic graphical models have become an important unsupervised learning tool for detecting network structures for a variety of problems, including the estimation of functional neuronal connectivity from two-photon calcium imaging data. However, in the context of calcium imaging, technological limitations only allow for partially overlapping layers of neurons in a brain region of interest to be jointly recorded. In this case, graph estimation for the full data requires inference for edge selection when many pairs of neurons have no simultaneous observations. This leads to the Graph Quilting problem, which seeks to estimate a graph in the presence of block-missingness in the empirical covariance matrix. Solutions for the Graph Quilting problem have previously been studied for Gaussian graphical models; however, neural activity data from calcium imaging are often non-Gaussian, thereby requiring a more flexible modeling approach. Thus, in our work, we study two approaches for nonparanormal Graph Quilting based on the Gaussian copula graphical model, namely a maximum likelihood procedure and a low-rank based framework. We provide theoretical guarantees on edge recovery for the former approach under similar conditions to those previously developed for the Gaussian setting, and we investigate the empirical performance of both methods using simulations as well as real data calcium imaging data. Our approaches yield more scientifically meaningful functional connectivity estimates compared to existing Gaussian graph quilting methods for this calcium imaging data set.

Related papers

• Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes [3.469001874498102]
  thesis focuses on data that has complex-temporal structure and on probabilistic graphical models that learn the structure in an interpretable and interpretable manner. practical applications of the methodology are considered using real datasets. This includes brain-connectivity analysis using data, space weather forecasting using solar imaging data, longitudinal analysis of public opinions using Twitter data, and mining of mental health related issues using TalkLife data.
  arXiv  Detail & Related papers  (2023-01-15T05:39:30Z)
• Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity [8.500141848121782]
  In many calcium imaging data sets, the full population of neurons is not recorded simultaneously, but instead in partially overlapping blocks. In this paper, we introduce a novel two-step approach to Graph Quilting, which first imputes the nuclear structure matrix using low-rank co completion. We show the efficacy of these methods for estimating connectivity from calcium imaging data.
  arXiv  Detail & Related papers  (2022-09-17T08:03:46Z)
• Deep Manifold Learning with Graph Mining [80.84145791017968]
  We propose a novel graph deep model with a non-gradient decision layer for graph mining. The proposed model has achieved state-of-the-art performance compared to the current models.
  arXiv  Detail & Related papers  (2022-07-18T04:34:08Z)
• Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning [102.9138736545956]
  Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
  arXiv  Detail & Related papers  (2022-04-30T12:57:02Z)
• Self-Supervised Graph Representation Learning for Neuronal Morphologies [75.38832711445421]
  We present GraphDINO, a data-driven approach to learn low-dimensional representations of 3D neuronal morphologies from unlabeled datasets. We show, in two different species and across multiple brain areas, that this method yields morphological cell type clusterings on par with manual feature-based classification by experts. Our method could potentially enable data-driven discovery of novel morphological features and cell types in large-scale datasets.
  arXiv  Detail & Related papers  (2021-12-23T12:17:47Z)
• Gaussian Graphical Model Selection for Huge Data via Minipatch Learning [1.2891210250935146]
  We propose the Minipatch Graph (MPGraph) estimator to solve the problem of graphical model selection. MPGraph is a generalization of thresholded graph estimators fit to tiny, random subsets of both the observations and the nodes. We prove that our algorithm achieves finite-sample graph selection consistency.
  arXiv  Detail & Related papers  (2021-10-22T21:06:48Z)
• Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity [1.52292571922932]
  We propose a hard thresholding operator to address the problem of graph selection in the presence of latent variables. We show that thresholding the graphical Lasso, neighborhood selection, or CLIME estimators have superior theoretical properties in terms of graph selection consistency. We also demonstrate the applicability of our approach through a neuroscience case study on calcium-imaging data to estimate functional neural connections.
  arXiv  Detail & Related papers  (2021-04-13T17:50:26Z)
• Multilayer Clustered Graph Learning [66.94201299553336]
  We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph. Experiments show that our method leads to a clustered clusters w.r.t. We learn a clustering algorithm for solving clustering problems.
  arXiv  Detail & Related papers  (2020-10-29T09:58:02Z)
• Line Graph Neural Networks for Link Prediction [71.00689542259052]
  We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. In this formalism, a link prediction problem is converted to a graph classification task. We propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
  arXiv  Detail & Related papers  (2020-10-20T05:54:31Z)
• A zero-inflated gamma model for deconvolved calcium imaging traces [10.661692149503788]
  Calcium imaging is a critical tool for measuring the activity of large neural populations. We propose a zero-inflated gamma (ZIG) model, which characterizes the calcium responses as a mixture of a gamma distribution and a point mass. We apply the resulting models to neural encoding and decoding problems.
  arXiv  Detail & Related papers  (2020-06-05T23:29:33Z)
• Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels [66.57101219176275]
  Disease diagnosis on chest X-ray images is a challenging multi-label classification task. We propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that presents a novel view of investigating the inter-dependency among different diseases. Our method is the first to build a graph over the feature maps with a dynamic adjacency matrix for correlation learning.
  arXiv  Detail & Related papers  (2020-02-26T17:10:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.