Weighted correlation network analysis ( WGCNA ) applied to multiple methylation data

Weighted correlation network analysis ( WGCNA ) applied to multiple methylation data www.phwiki.com

Weighted correlation network analysis ( WGCNA ) applied to multiple methylation data

Hanson, Jonathan, Executive Editor has reference to this Academic Journal, PHwiki organized this Journal Empirical Evaluation of Correlation Network Methods Applied to Genomic DataSteve Horvath Acknowledgement:Lin Song (dissertation)+Peter LangfelderAging effects on DNA methylation modules in human brain in addition to blood tissue. Genome Biol. 2012 Oct 3;13(10):R97. PMID: 23034122When Is Hub Gene Selection Better than St in addition to ard Meta-Analysis Langfelder et al (2013) PLoS ONE 8(4): e61505. Comparison of co-expression measures: mutual in as long as mation, correlation, in addition to model based indicesL. Song, P. Langfelder et al 2012 BMC Bioin as long as matics (2012), 13:328.Comparing Statistical Methods as long as Constructing Large Scale Gene Networks (2012) Allen JD, Xie Y, Chen M, Girard L, Xiao G PLoS ONE 7(1): e29348. doi:10.1371ContentAging effects on DNA methylation modules in human brain in addition to blood tissue. Genome Biol. 2012 Oct 3;13(10):R97. PMID: 23034122Content

Odessa National Maritime Academy UA www.phwiki.com

This Particular University is Related to this Particular Journal

Steve HorvathSenior author: Roel A Ophoff UCLAAging effects on DNA methylation modules in human brain in addition to blood tissue. Weighted correlation network analysis (WGCNA) applied to multiple methylation data Goal: find age related consensus methylation modules in whole blood in addition to brain tissueConstruct a signed weighted correlation network based on multiple, independent data setsPurpose: keep track of relationships between genes2. Identify consensus modules Purpose: find robustly defined in addition to reproducible modules3. Relate modules to external in as long as mationAgeGene In as long as mation: gene ontology, cell marker genesPurpose: find biologically interesting age related modulesAnalysis steps of WGCNA

Additional public methylation data used as long as consensus module analysisInf 27k Illumina Array1) Rakyan et al GSE2023693 different healthy females (31 twin pairs in addition to 31 singletons)ranging from 49 to 75 years of ageWhole blood2) Healthy individuals from UK ovarian cancer dataTeschendorff et al 2010, Song et al 2009 GSE19711267 healthy women3) Type 1 Diabetics (Irish)93 males, 95 females 4) Brain data: about 150 individuals, 4 brain regionsR codeblockwiseConsensusModules in the WGCNA R package, see the tutorials Message: green module contains probes positively correlated with age

R code as long as finding hub genes consensusKME functionOutput consensusKME

Gene ontology enrichment analysis of the green moduleHighly significant enrichment in multiple terms related to cell differentiation, development in addition to brain functione.g. nervous system development (Bonferroni corrected p-value =2.1e-6), neuron differentiation (p=7.6e-5), anatomical structure development (p=0.00013), cell development (p=0.00024), generation of neurons (p=0.00038), neurogenesis (p=0.00052), cell differentiation (p=0.00057). Green module is enriched with neuronal cell type markersGreen module genes are enriched as long as genes that areupregulated in neurons (based on Cahoy et al 2009) (Bonferroni corrected p-values p=5e-9) Related to CA1 area related neurons (based on Newrzella et al 2007) (p=3e-7), downregulated in hippocampus in early Alzheimer’s disease (Parachikova et al 2007) (p=3e-6)Recall that this module can also be found in blood tissue.Polycomb-group proteinsPolycomb Group gene expression is important in many aspects of development.Genes that are hypermethylated with age are significantly enriched with Polycomb group target genes (Teschendorff et al 2010)This insight allows us to compare different gene selection strategies.The higher the enrichment with respect to PCGT genes the more signal is in the data.

Comparison of st in addition to ard screening vs. consensusKME screening with respect to enrichment as long as PCG target geneWhen Is Hub Gene Selection Better than St in addition to ard Meta-Analysis Langfelder et al (2013) PLoS ONE 8(4): e61505. When does hub gene selection lead to more meaningful gene lists than a st in addition to ard statistical analysis based on significance testingHere we address this question as long as the special case when multiple data sets are available. This is of great practical importance since as long as many research questions multiple gene expression or other -omics data sets are publicly available. In this case, the data analyst can decide between a st in addition to ard statistical approach (e.g., based on meta-analysis) in addition to a co-expression network analysis approach that selects intramodular hubs in consensus modules.

Intramodular hub genes versus whole network hubsIntramodular hubs have high intramodular connectivity kME with respect to a given module of interestWhole network hubs have high values of whole network connectivity kk= row sum of the adjacency matrixk= number of direct neighbors in case of an unweighted networkQ & A1. Are whole-network hub genes relevant or should one exclusively focus on intramodular hubs Answer: Focus exclusively on intramodular hubs in trait-related modules. 2. Do network-based gene selection strategies lead to gene lists that are biologically more in as long as mative than those based on a st in addition to ard marginal approaches Answer: Yes, gene selection based on intramodular connectivity leads to biologically more in as long as mative gene lists than marginal approaches. 3. Do network-based gene selection strategies lead to gene lists that have more reproducible trait associations than those based on a st in addition to ard marginal approaches Answer: Overall no. But in case of a weak signal networks can help.Criteria as long as judging gene selection methodsCriterion 1 evaluates the biological insights gained, i.e. it is relevant in basic research. Criterion 2 evaluates the validation success in independent data sets, i.e. it is relevant when it comes to developing diagnostic or prognostic biomarkers.

Data sets used in the empirical evaluationWe compare st in addition to ard meta-analysis with consensus network analysis in three comprehensive in addition to unbiased empirical studies: (1) Find genes predictive of lung cancer survivalGold st in addition to ard=cell proliferation related genes(2) Find age related DNA methylation markersGold st in addition to ard= Polycomb group target genes(3) Find genes related to total cholesterol in mouse liver tissuesGold st in addition to ard= immune system related genes R code in the WGCNA packageFor st in addition to ard screening, we used the metaAnalysis function For finding hubs in consensus modules, we used the consensusKME function

Hanson, Jonathan Overland Journal Executive Editor www.phwiki.com

ResultsThe results demonstrate that intramodular hub gene status is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, meta-analysis methods per as long as m as good as (if not better) than a co-expression network approach in terms of validation success (criterion 2).Comparison of co-expression measures: mutual in as long as mation, correlation, in addition to model based indicesL. Song, P. Langfelder et al 2012 BMC Bioin as long as matics (2012), 13:328.Content

Network=Adjacency MatrixA network can be represented by an adjacency matrix, A=[aij], that encodes whether/how a pair of nodes is connected.A is a symmetric matrix with entries in [0,1] For unweighted network, entries are 1 or 0 depending on whether or not 2 nodes are adjacent (connected)For weighted networks, the adjacency matrix reports the connection strength between node pairsOur convention: diagonal elements of A are all 1.Two types of weighted correlation networksDefault values: =6 as long as unsigned in addition to =12 as long as signed networks.We prefer signed networks Zhang et al SAGMB Vol. 4: No. 1, Article 17.Measures the mutual dependency of two r in addition to om variables.For categorical variable DX, DY: p(ldxr), p(ldyc) st in addition to s as long as the frequency of the r-th in addition to c-th level of dx, dy. Entropy(dx, dy) st in addition to s as long as the joint entropy of variable dx in addition to dy.For continuous variable, e.g. gene expression data, it is not straight as long as ward to estimate MI. Need to choose variable discretization method.Need to choose entropy estimation method.Estimate of MI depends on sample size: poor estimate as long as small sample sizeEstimation is computational challenging.Mutual in as long as mation: an in as long as mation theory concept> =0

The per as long as mance in constructing gene regulatory network in E. coli. Figure 6. Allen JD, et al. (2012) PLoS ONE 7(1): e29348. doi:10.1371/journal.pone.0029348WGCNA per as long as ms consistently well across different choices of the specificity

Hanson, Jonathan Executive Editor

Hanson, Jonathan is from United States and they belong to Overland Journal and they are from  Prescott, United States got related to this Particular Journal. and Hanson, Jonathan deal with the subjects like Motorcycles; Off-Road Vehicles

Journal Ratings by Odessa National Maritime Academy

This Particular Journal got reviewed and rated by Odessa National Maritime Academy and short form of this particular Institution is UA and gave this Journal an Excellent Rating.