Cytoscape tutorial adjacency matrix9/13/2023 This data was gathered from a skin ageing study and has been processed and normalized with the R script provided in Additional data n☁0 of the corresponding article. In this vignette, we use the microarray data set GSE85358 from the Kuehne et al. study. ![]() Moreover, the expression data have to be normalized and transcripts expression reduced to the gene level (See How can I reduce my transcriptomic data to the gene level ? since GWENA is designed to build gene co-expression networks. To read this file with R, use the appropriate function according to the data separator (e.g. read.csv, read.table). Expression data have to be stored as text or spreadsheet files and formatted with genes as columns and samples as rows. GWENA support expression matrix data coming from either RNA-seq or microarray experiments. This document gives a brief tutorial using a subset of a microarray data set to show the content and value of each step in the pipeline. Networks comparison: if multiple conditions are available (time points, treatments, phenotype, etc.), analysis of modules preservation/non-preservation between conditions can be performed.Graph visualization and topological analysis: hub genes are identified, as well as visualization of modules.Biological integration: gene set enrichment analysis and phenotypic association (if phenotypes are provided) are performed on modules.Modules detection: groups of genes with closest similarity scores are detected as modules.Network building: a matrix of similarity score is computed between each gene with Spearman correlation, then transformed into an adjacency matrix, and finally into a topological overlap matrix.Gene filtering: data is filtered according to the transcriptomic technology used.Input: data is provided as a ame or a matrix of expression intensities (pre-normalized). ![]() Using transcriptomics data from either RNA-seq or microarray, the package follows the steps displayed in Figure 1: This pipeline includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparisons of networks between conditions. GWENA (Gene Whole co-Expression Network Analysis) is an R package to perform gene co-expression network analysis in a single pipeline.
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