Integration of PloGO2 and WGCNA for proteomics

This vignette describes a tailored workflow of using WGCNA for protein network analysis. A publicly available protein ratio dataset was used to demonstrate the major workflow steps and its outputs Wu et al. (2016). It also demonstrates that the WGCNA output can be seamlessly input into PloGO2 and further characterised functionally using PloGO2. 1 WGCNA workflow for proteomics WGCNA Langfelder and Horvath (2008) is a popular correlation network functional analysis tool which can be used to generate a potentially large number of related data subsets via clustering. The WGCNA was proposed and mostly used for genomic data correlation analysis. We tailored the WGCNA workflow to be more suitable for proteomic data analysis, and include this wrapper in the PloGO2 package for completeness. Our in-house tailored WGNCA workflow starts from a protein abundance or ratio dataset. It is assumed that the data has been properly normalised. The protein expression will be logtransformed before the analysis starts. Firstly, a soft power was selected for constructing the weighted correlation matrix by using the approximate scale-free network criteria. A scale free network is one where the topology is dominated by a few highly connected nodes, which link the rest of the less connected nodes to the system; it is assumed that most biologically relevant networks should satisfy this property. By raising the correlation to the selected soft power, the correlation network becomes scale-free. The cut-off of the parameter RsquaredCut was set as 0.85. Signed networks, instead of unsigned for gene network analysis, are constructed using the soft power selected. Then TOM (topology overlap metrics) distance was calculated from the network adjacency. Hierarchical clustering was performed based on the TOM distance. A set of clusters were obtained by using the dynamic tree cutting method with the parameter value of minClusterSize as 20. An automatic cluster merging function was invoked to merge the closely correlated clusters from the dynamic tree cutting. The script for an example WGNCA workflow can be found in the script folder of this package. An example of the input of WGCNA is as follows. > library(PloGO2) > library(xtable) > path print(xtable(read.csv(file.path(path,"rice.csv"))[1:6,c(1:2,5:7)]))