02197nas a2200181   4500008004100000022001400041245011200055210006900167260001600236490000700252520151000259100002001769700002201789700003501811700002001846700002001866856012901886       2023                        eng d  a2079-773700aSigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types.0 aSigPrimedNet A SignalingInformed Neural Network for scRNAseq Ann  c2023 Apr 100 v123 a<p>Single-cell RNA sequencing is increasing our understanding of the behavior of complex tissues or organs, by providing unprecedented details on the complex cell type landscape at the level of individual cells. Cell type definition and functional annotation are key steps to understanding the molecular processes behind the underlying cellular communication machinery. However, the exponential growth of scRNA-seq data has made the task of manually annotating cells unfeasible, due not only to an unparalleled resolution of the technology but to an ever-increasing heterogeneity of the data. Many supervised and unsupervised methods have been proposed to automatically annotate cells. Supervised approaches for cell-type annotation outperform unsupervised methods except when new (unknown) cell types are present. Here, we introduce SigPrimedNet an artificial neural network approach that leverages (i) efficient training by means of a sparsity-inducing signaling circuits-informed layer, (ii) feature representation learning through supervised training, and (iii) unknown cell-type identification by fitting an anomaly detection method on the learned representation. We show that SigPrimedNet can efficiently annotate known cell types while keeping a low false-positive rate for unseen cells across a set of publicly available datasets. In addition, the learned representation acts as a proxy for signaling circuit activity measurements, which provide useful estimations of the cell functionalities.</p>1 aGundogdu, Pelin1 aAlamo, Inmaculada1 aNepomuceno-Chamorro, Isabel, A1 aDopazo, Joaquin1 aLoucera, Carlos  uhttp://clinbioinfosspa.es/content/sigprimednet-signaling-informed-neural-network-scrna-seq-annotation-known-and-unknown-cell03231nas a2200193   4500008004100000022001400041245011800055210006900173260001600242300000600258490000700264520252200271100002002793700002002813700003002833700002002863700002302883856013102906       2022                        eng d  a1756-038100aIntegrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data.0 aIntegrating pathway knowledge with deep neural networks to reduc  c2022 Jan 03  a10 v153 a<p><b>BACKGROUND: </b>Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications of scRNA-seq data analysis is the identification of new cell types and cell states. Deep neural networks (DNNs) are among the best methods to address this problem. However, this performance comes with the trade-off for a lack of interpretability in the results. In this work we propose an intelligible pathway-driven neural network to correctly solve cell-type related problems at single-cell resolution while providing a biologically meaningful representation of the data.</p><p><b>RESULTS: </b>In this study, we explored the deep neural networks constrained by several types of prior biological information, e.g. signaling pathway information, as a way to reduce the dimensionality of the scRNA-seq data. We have tested the proposed biologically-based architectures on thousands of cells of human and mouse origin across a collection of public datasets in order to check the performance of the model. Specifically, we tested the architecture across different validation scenarios that try to mimic how unknown cell types are clustered by the DNN and how it correctly annotates cell types by querying a database in a retrieval problem. Moreover, our approach demonstrated to be comparable to other less interpretable DNN approaches constrained by using protein-protein interactions gene regulation data. Finally, we show how the latent structure learned by the network could be used to visualize and to interpret the composition of human single cell datasets.</p><p><b>CONCLUSIONS: </b>Here we demonstrate how the integration of pathways, which convey fundamental information on functional relationships between genes, with DNNs, that provide an excellent classification framework, results in an excellent alternative to learn a biologically meaningful representation of scRNA-seq data. In addition, the introduction of prior biological knowledge in the DNN reduces the size of the network architecture. Comparative results demonstrate a superior performance of this approach with respect to other similar approaches. As an additional advantage, the use of pathways within the DNN structure enables easy interpretability of the results by connecting features to cell functionalities by means of the pathway nodes, as demonstrated with an example with human melanoma tumor cells.</p>1 aGundogdu, Pelin1 aLoucera, Carlos1 aAlamo-Alvarez, Inmaculada1 aDopazo, Joaquin1 aNepomuceno, Isabel  uhttp://clinbioinfosspa.es/content/integrating-pathway-knowledge-deep-neural-networks-reduce-dimensionality-single-cell-rna-seq