For the weighted gene co-expression network analysis (WGCNA), the R package WGCNA was used to con- struct gene modules with genes that were co-expressed. Also contains several functions to perform differential. Contains functions to filter, process, save, visualize, and interpret differential correlations of identifier-pairs across the entire identifier space, or with respect to a particular set of identifiers (e.g., one). Performs differential correlation analysis on input matrices, with multiple conditions specified by a design matrix.
Wgcna R Package Software Package Iscreate_mae: Create MultiAssayExperiment object for MetaboDiff The WGCNA R software package is a comprehensive collection of R functions for performing various. Zip Source (Linux, Mac etc): WGCNA0. WGCNA was originally built for the analysis of bulk gene expression datasets, and the performance of vanilla WGCNA on single-cell data is limited due to the inherent sparsity of 02201 Sec. ScWGCNA is a bioinformatics workflow and an add-on to the R package WGCNA to perform weighted gene co-expression network analysis in single-cell or single-nucleus RNA-seq datasets.![]() quality_plot: Quality plot of processing steps outlier_heatmap: Heatmap to visualize outliers in the study set normalize_met: Normalize metabolomic data by vsn name_modules: Name metabolic correlation modules na_heatmap: Heatmap to visualize missing metabolites across the samples ![]() Modules) differ between groups? | metabolic correlation network analysis - module significance (MS) plotWhich metabolite is most closely related to the individual subpathway (i.e. Biological questionsThe following table summarizes the biological questions that can be answered by MetaboDiff functions.- | -Are there outliers in my data set? | outlier heatmap including k-means clusteringAre there metabolome-wide changes between samples? | unsupervised analyses (PCA, tSNE)Which individual metabolites show differential abundance between groups? | hypothesis testing with correction for multiple testingWhich metabolic subpathways (i.e. Hence, some functionality might not be available with smaller data sets < 50 metabolites. Please note that the data analysis has been developed for metabolomic data sets of more than 100 metabolites. You will only be able to plot the volcano plot after executing the function for differential analysis ( diff_test). Certain plotting functions will only be available once the corresponding statistics has been run, e.g. Mac ps3 emulatorHypothesis testing can be simultaneously performed for multiple groups. The p-values are corrected for multiple testing by the Benjamini-Hochberg procedure. If there are more than two groups an analysis of variance (ANOVA) is applied. If two groups are compared the function applies the Student`s T-Test. Hypothesis testingDifferential analysis for individual metabolites is performed with the function diff_test. The first two dimensions in the tSNE plot do not reveal a distinct difference in the metabolomic profiles between the normal and tumor samples. name_modules - name metabolic correlation modules identify_modules - identification of metabolic correlation modules diss_matrix - construction of dissimilarity matrix Within MetaboDiff, metabolic correlation network analysis is performed by a set of functions: Female after multiple testing with a cutoff of p-value<0.05 (figure Metabolic correlation network analysis ImplementationTo derive meaningful subpathways that are enriched between groups, MetaboDiff generates a metabolic correlation network. Cancer Research, 74(24), 7198–7204. AKT1 and MYC Induce Distinctive Metabolic Fingerprints in Human Prostate Cancer. R., Zadra, G., Photopoulos, C., et al. Electrophoresis, 36(24), 3050–3060. Missing value imputation strategies for metabolomics data. G., Godzien, J., Alonso-Herranz, V., L pez-Gonz lvez, N., & Barbas, C. 2014 Jan 42(Database issue):D478-84.: Armitage, E. SMPDB 2.0: Big Improvements to the Small Molecule Pathway Database. R package version 1.2.1: Jewison T, Su Y, Disfany FM, et al. Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Bioinformatics, 18 Suppl 1, S96–104.: Ignatiadis, N., Klaus, B., Zaugg, J. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. BMC Bioinformatics, 9, 559–559. WGCNA: an R package for weighted correlation network analysis. : Langfelder, P., & Horvath, S. Statistical Applications in Genetics and Molecular Biology, 4(1), Article17. A general framework for weighted gene co-expression network analysis. : Zhang, B., & Horvath, S. BMC Bioinformatics, 15 Suppl 15(Suppl 15), S3. Gene differential coexpression analysis based on biweight correlation and maximum clique. Geometric Interpretation of Gene Coexpression Network Analysis. Bioinformatics, 24(5), 719–720.: Horvath, S., & Dong, J. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R.
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