Average enrichment of TFs and selected TF undergroups (see “Comparative enrichment and gene-ontology analysis”) as function of <i>s</i>-core index <i>n</i> for: (a) Human all-tissues network <i>H</i>_<i>A</i>, (b) mouse all-tissues network <i>M</i>_<i>A</i>, (c) human brain network <i>H</i>_<i>B</i>, and (d) mouse brain network <i>M</i>_<i>B</i>.

<p>Large <i>s</i>-core index values indicate high centrality in the network. TFs and HOMs are significantly enriched for both human and mouse all-tissues networks. The enrichment of TFs is trivially 1 for the brain networks.</p

L'Auto-vélo : automobilisme, cyclisme, athlétisme, yachting, aérostation, escrime, hippisme / dir. Henri Desgranges

19 septembre 19381938/09/19 (A39,N13787)

Comparative analysis of weighted gene co-expression networks in human and mouse

<div><p>The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-level investigation of genotype-phenotype relationships in a wide range of systems. Since mouse is an important model organism for biomedical research on human disease, it is of great interest to identify similarities and differences in the functional roles of human and mouse orthologous genes. Here, we develop a novel network comparison approach which we demonstrate by comparing two gene-expression data sets from a large number of human and mouse tissues. The method uses weighted topological overlap alongside the recently developed network-decomposition method of <i>s</i>-core analysis, which is suitable for making gene-centrality rankings for weighted networks. The aim is to identify globally central genes separately in the human and mouse networks. By comparing the ranked gene lists, we identify genes that display conserved or diverged centrality-characteristics across the networks. This framework only assumes a single threshold value that is chosen from a statistical analysis, and it may be applied to arbitrary network structures and edge-weight distributions, also outside the context of biology. When conducting the comparative network analysis, both within and across the two species, we find a clear pattern of enrichment of transcription factors, for the homeobox domain in particular, among the globally central genes. We also perform gene-ontology term enrichment analysis and look at disease-related genes for the separate networks as well as the network comparisons. We find that gene ontology terms related to regulation and development are generally enriched across the networks. In particular, the genes FOXE3, RHO, RUNX2, ALX3 and RARA, which are disease genes in either human or mouse, are on the top-10 list of globally central genes in the human and mouse networks.</p></div

Relationship between <i>t</i>(<i>S</i>₁, <i>S</i>₂) and <i>u</i>(<i>S</i>₁, <i>S</i>₂) normalized according to the number of nodes <i>N</i> = <i>N</i>_{<i>S</i>₁∩<i>S</i>₂}.

<p>A node <i>i</i> must be within the gray, diamond-shaped area because of the limitations from Eqs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187611#pone.0187611.e006" target="_blank">2</a>) and (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187611#pone.0187611.e007" target="_blank">3</a>). The dark gray areas contain nodes where: (A) and are large, i.e. the node is central in both <i>s</i>-core+ sequences 1 and 2. (B) is large, while is small, i.e. the node is central in <i>s</i>-core+ sequence 1 and peripheral in sequence 2. (C) is large, while is small, which is the opposite of the (B) criteria, i.e. the node is central in <i>s</i>-core+ sequence <i>S</i>₂, but peripheral in sequence <i>S</i>₁.</p

Node density plots of <i>u</i>(<i>S</i>₁, <i>S</i>₂) versus <i>t</i>(<i>S</i>₁, <i>S</i>₂) for comparisons between the <i>s</i>-core+ sequences in: (a) human all-tissues and brain networks, (b) mouse all-tissues and brain networks, (c) human and mouse all-tissues network and (d) human and mouse brain networks.

<p>The densities are given by their <i>z</i>-score value (see “<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187611#sec026" target="_blank">Methods</a>”). There is a clear statistical over-representation of genes that are central in both <i>s</i>-core+ sequence <i>S</i>₁ and <i>S</i>₂ for (a), (b) and (c). The brain-networks comparison in (d) show no clear trend.</p

Enrichment of TFs for the all-tissues and brain comparison across species.

<p>The left column show (<i>H</i>_<i>A</i>, <i>M</i>_<i>A</i>) comparison, while the right compare (<i>H</i>_<i>B</i>, <i>M</i>_<i>B</i>). Note (d), comparison of human and mouse brain network. For the innermost 10% of the genes, KZNFs are enriched in the comparison sequence <i>u</i>_<i>i</i>(<i>H</i>_<i>B</i>, <i>M</i>_<i>B</i>). (e) Enrichment of KZNFs according to <i>u</i>_<i>i</i>(<i>M</i>_<i>A</i>, <i>H</i>_<i>A</i>), which contrasts with (d). The x-axis is displayed in descending order, since <i>r</i> is the number of innermost genes, meaning that <i>r</i>/<i>N</i> = 1 denote the entire gene set, while smaller <i>r</i>/<i>N</i> denote gene sets of increasing centrality.</p

Jaccard index <i>J</i>(<i>S</i>₁, <i>S</i>₂) as function of node subsets of <i>s</i>-core+ sequence <i>S</i>₁ and <i>S</i>₂ consisting of their <i>m</i> respective innermost nodes, for: (a) Human all-tissues and brain, (b) mouse all-tissues and brain, (c) human and mouse all-tissues and (d) human and mouse brain network comparisons.

<p>The dashed lines are the expectation value for <i>J</i>(<i>m</i>; <i>S</i>₁, <i>S</i>₂). Both within-species comparisons show large <i>J</i>-values for small <i>m</i>, while the within-species comparisons have a lesser extent of innermost core overlap.</p