Question: Wgcna_How To Calculate Eigengenes From Multiple Data Sets (Gene Expression Profiles)?
1
gravatar for chae
5.7 years ago by
chae40
chae40 wrote:

I am trying to figure out some traits using WGCNA methodology.

This analysis is based on microarray meta-analysis. Briefly explaining, I did analyze some features of differentially expressed genes from integrating independent data set (microarray data set containing profiles of patients and healthy subjects) using random effect inverse-variance methodology based on effect size.

In WGCNA procedure, I took specific value per gene by

1) calculate PCC from gene expression profiles per each data set,  
2) converting PCC into Fisher's Z-statistic from each data set, 
3) integrating different data sets using random effect inverse-variance methodology, and then 
4) converting back to correlation coefficient using the reverse Fisher's Z-statistic.

To identify co-expressed network, I considered this reversed correlation coefficient as adjacency value and follow typical R WGCNA commands.

However, I don't have any idea about the way to calculate the principle components from gene expression profiles.

Actually, all procedures to combine independent different data sets were built on gene-centric view. As you may know well, one of biggest challenges in meta-analysis is inherent and naturally-deriven variability and variance between each data set. To calculate eigengenes from raw gene expression profiles, what should I do? I've thought about the unified methodology such as z-value transformation per each data set and treat the z-value as raw gene expression profiles. However, I am not sure whether this method is relevant to follow it.

genes correlation • 2.6k views
ADD COMMENTlink modified 5.6 years ago by Biostar ♦♦ 20 • written 5.7 years ago by chae40
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.3.0
Traffic: 1392 users visited in the last hour