This might be more of a "wish list" post, but I am interested in the capacity of carrying out correlation network analysis in JMP with OMICS datasets (in my case, protein concentration data). There is a popular approach called WGCNA (weighted gene co-expression network analysis), described as follows (from https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/):
"Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial."
It seems to me that this is just a fancy way of clustering by covariance, which I DO know how to do with JMP Pro 14 (I've looked at clustering across my sample set=biological replicates alongside clustering of the various proteins whose concentrations I measured in each sample). Basically, I like what the authors have done in the first figure I've attached:: clustered genes into modules based on covariance and then looked at correlation against categorical properties (time, temperature, and thermotolerance). In this example, the dakgrey module featured 2,053 genes and was positively correlated with temperature. I feel like this sort of "sexified" bioinformatics analysis should be achievable in JMP Pro (or even in regular JMP), right? I am wondering if their "module membership measure" is essentially the eigenvector value or something along those lines. From my data
the samples (left side) are in two clusters based on their proteome profiles, whereas on the x axis, you can see 10-12 general clusters of proteins (I could transpose the dataset and have each protein assigned a cluster).
Basically, I want to look at module (cluster?) correlations across temperature, time, and genotype in this attached dataset to where I can identify clusters of proteins that correlate with these experimental factors of interest. It actually seems like partial least squares could be used for this, too.....Any ideas out there?