The EntOptLayout Cytoscape plug-in for the efficient visualization of major protein complexes in protein–protein interaction and signalling networks

Ágg, Bence; Császár, Andrea; Szalay-Bekő, Máté; Veres, Dániel V.; Mizsei, Réka; Ferdinándy, Péter; Csermely, Péter; Kovács, I.

doi:10.1093/bioinformatics/btz257

Cited by 29 publications

(25 citation statements)

References 11 publications

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Section: Resultsmentioning

confidence: 99%

“… (A-C) Network representations using the EntOpt layout plugin in Cytoscape [ 27 ]. (A) Color and shape of vertices represent neurotransmitter expression and modality of neurons, respectively (see inset for definitions).…”

Section: Resultsmentioning

confidence: 99%

“…Network representations of synaptic polarities in the C. elegans ionotropic chemical synapse connectome using the EntOptLayout plugin of Cytoscape [27] are in Fig 5. Fig 5A shows that the modular structure of the C. elegans connectome visualized by the EntOptLayout method nicely captures the anatomical locations of the anterior, ventral and lateral ganglions, as well as the premotor interneurons of the worm.…”

Section: Network Representations Of the Signed C Elegans Connectomementioning

confidence: 99%

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Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network

et al. 2020

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Graph theoretical analyses of nervous systems usually omit the aspect of connection polarity, due to data insufficiency. The chemical synapse network of Caenorhabditis elegans is a well-reconstructed directed network, but the signs of its connections are yet to be elucidated. Here, we present the gene expression-based sign prediction of the ionotropic chemical synapse connectome of C. elegans (3,638 connections and 20,589 synapses total), incorporating available presynaptic neurotransmitter and postsynaptic receptor gene expression data for three major neurotransmitter systems. We made predictions for more than two-thirds of these chemical synapses and observed an excitatory-inhibitory (E:I) ratio close to 4:1 which was found similar to that observed in many real-world networks. Our open source tool (http://EleganSign.linkgroup.hu) is simple but efficient in predicting polarities by integrating neuronal connectome and gene expression data.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Network Representations Of the Signed C Elegans Connectomementioning

confidence: 99%

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Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network

et al. 2020

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“…We next overlaid existing PPI evidence to further refine the network by retaining only gene pairs from the STRING database 25,26 . An algorithm providing efficient visualization of different network modules, EntOptLayout 27 , was applied and the network revealed several cell type related modules that can be associated with the known biology in our benchmarking dataset (Fig. 5C).…”

Section: Gene-gene Correlation Network Inferred From Single-cell Rna-mentioning

confidence: 99%

“…The final network was visualized using Cytoscape 37 together with R package RCy3 38 . The network layout was generated using EntOptLayout Cytoscape plug-in 27 . Figure 1.…”

Section: Network Reconstructionmentioning

confidence: 99%

Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing

Zhang

Atwal

Lim

2020

Preprint

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With the rapid advancement of single-cell RNA-seq (scRNA-seq) technology, many data preprocessing methods have been proposed to address numerous systematic errors and technical variabilities inherent in this technology. While these methods have been demonstrated to be effective in recovering individual gene expression, the suitability to the inference of gene-gene associations and subsequent gene networks reconstruction have not been systemically investigated. In this study, we benchmarked five representative scRNA-seq normalization/imputation methods on human cell atlas bone marrow data with respect to their impact on inferred gene-gene associations. Our results suggested that a considerable amount of spurious correlations was introduced during the data preprocessing steps due to over-smoothing of the raw data. We proposed a model-agnostic noise regularization method that can effectively eliminate the correlation artifacts. The noise regularized gene-gene correlations were further used to reconstruct gene co-expression network and successfully revealed several known immune cell modules.

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Computational Inference of Synaptic Polarities in Neuronal Networks

2022

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Synaptic polarity, that is, whether synapses are inhibitory (−) or excitatory (+), is challenging to map, despite being a key to understand brain function. Here, synaptic polarity is inferred computationally considering three experimental scenarios, depending on the nature of available input data, using the Caenorhabditis elegans connectome as an example. First, the inputs consist of detailed neurotransmitter (NT) and receptor (R) gene expression, integrated through the connectome model (CM). The CM formulates the problem through a wiring rule network that summarizes how NT-R pairs govern synaptic polarity, and resolves 356 synaptic polarities in addition to the 1752 known polarities. Second, known synaptic polarities are considered as an input, in addition to the NT and R gene expression data, but without wiring rules. These data train the spatial connectome model, which infers the polarity of 81% of the CM-resolved connections at > 95% precision, while also inferring 147 of the remaining unknown polarities. Last, without known expression or wiring rules, polarities are inferred through a network sign prediction problem. As an illustration of high performance in this case, the generalized CM is introduced. These results address imminent challenges in unveiling large-scale synaptic polarities, an essential step toward more realistic brain models.

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The EntOptLayout Cytoscape plug-in for the efficient visualization of major protein complexes in protein–protein interaction and signalling networks

Cited by 29 publications

References 11 publications

Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network

Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network

Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing

Computational Inference of Synaptic Polarities in Neuronal Networks

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