{"title":"Autoencoder Denoising for Network-Based Spatial Transcriptomics Data with Applications for Cell Signaling Estimation.","authors":"Azka Javaid, H Robert Frost","doi":"10.1007/978-3-032-16719-4_16","DOIUrl":"10.1007/978-3-032-16719-4_16","url":null,"abstract":"<p><p>We propose an autoencoder-based framework for denoising networks estimated from Spatial Transcriptomics (ST) data for cell signaling analysis. Our method consists of an unsupervised encoder-decoder framework for denoising the network adjacency matrix and a supervised framework for cell signaling estimation. We validate our denoising component using the Frobenius norm metric for graphs simulated using the Barabási-Albert (BA) and Erdős-Rényi (ER) models against reconstructions generated using the singular value decomposition (SVD). We then validate the cell signaling estimates generated using the supervised component on real ST data for the Wnt3-Fzd1 and Ephb1-Efnb3 interactions. We report that our framework achieves better adjacency matrix reconstructions for superlinear BA and dense ER graphs and generates cell signaling estimates that are both regionally specific and biologically plausible. An important contribution of this work is the application of neural networks for network-based cell signaling estimation using ST data and the benchmarking of autoencoder versus SVD denoising for different graph models.</p>","PeriodicalId":523858,"journal":{"name":"Complex networks & their applications. International Conference on Complex Networks and Their Applications","volume":"1266 ","pages":"196-207"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13191374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148020547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Eigenvector centrality for multilayer networks with dependent node importance.","authors":"H Robert Frost","doi":"10.1007/978-3-031-53472-0_1","DOIUrl":"10.1007/978-3-031-53472-0_1","url":null,"abstract":"<p><p>We present a novel approach for computing a variant of eigenvector centrality for multilayer networks with inter-layer constraints on node importance. Specifically, we consider a multilayer network defined by multiple edge-weighted, potentially directed, graphs over the same set of nodes with each graph representing one layer of the network and no inter-layer edges. As in the standard eigenvector centrality construction, the importance of each node in a given layer is based on the weighted sum of the importance of adjacent nodes in that same layer. Unlike standard eigenvector centrality, we assume that the adjacency relationship and the importance of adjacent nodes may be based on distinct layers. Importantly, this type of centrality constraint is only partially supported by existing frameworks for multilayer eigenvector centrality that use edges between nodes in different layers to capture inter-layer dependencies. For our model, constrained, layer-specific eigenvector centrality values are defined by a system of independent eigenvalue problems and dependent pseudo-eigenvalue problems, whose solution can be efficiently realized using an interleaved power iteration algorithm. An R package implementing this method along with example vignettes is available at https://hrfrost.host.dartmouth.edu/CMLC/.</p>","PeriodicalId":523858,"journal":{"name":"Complex networks & their applications. International Conference on Complex Networks and Their Applications","volume":"1143 ","pages":"3-14"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13184887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}