I am working on a genome-wide study and struggling with gene co-expression network analysis. I am working on Tomato(Solanum lycopersicum). Can someone suggest me some tools or methods for this analysis? Currently, I am trying to use the ATTED II tool for this but I am not sure that the results will be good enough.

I tried to use RNA seq data and WebChemitool but there is some kind of error and I couldn't find any way to solve the problem. Please can someone tell me what can I do now?

https://doi.org/10.18632/aging.202969

https://pubmed.ncbi.nlm.nih.gov/33962394

Abstract

The ketogenic diet has been widely used in the treatment of various nervous system and metabolic-related diseases. Our previous research found that a ketogenic diet exerts a protective effect and promotes functional recovery after spinal cord injury. However, the mechanism of action is still unclear. In this study, different dietary feeding methods were used, and myelin expression and gene level changes were detected among different groups. We established 15 RNA-seq cDNA libraries from among 4 different groups. First, KEGG pathway enrichment of upregulated differentially expressed genes and gene set enrichment analysis of the ketogenic diet and normal diet groups indicated that a ketogenic diet significantly improved the steroid anabolic pathway in rats with spinal cord injury. Through cluster analysis, protein-protein interaction analysis and visualization of iPath metabolic pathways, it was determined that Sqle, Sc5d, Cyp51, Dhcr24, Msmo1, Hsd17b7, and Fdft1 expression changed significantly. Second, through weighted gene co-expression network analysis showed that rats fed a ketogenic diet showed a significant reduction in the expression of genes involved in immune-related pathways, including those associated with immunity and infectious diseases. A ketogenic diet may improve the immune microenvironment and myelin growth in rats with spinal cord injury through reprogramming of steroid metabolism.

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Open Access: False

Authors: Hong Zeng - Yao Lu - Meng-Jie Huang - Yan-Yan Yang - Hua-Yi Xing - Xiao-Xie Liu - Mou-Wang Zhou -

Additional links: None found

I'm working with gene expression correlations. For the tool we're developing, we need some sort of package capable of calculating adjusted p-values only. Packages such as rcorr and corr.test simultenaously calculate correlation coefficients and p-values. We do this and extract the p-values for verification, but we are making a tool that requires a step to calculate just the p-values, and due to underlying parts of our code that I'm not mentioning, the tool at this stage of the process can return only one matrix. If we use corr.test or similar, the one matrix returned is always correlation coefficient. We need it to be p-vals.

thanks !

For the coexpression networks where you got best results, how did you choose coexpression cutoff? Would you do the same again? If not, what is your current method of choice?

Are there non-cutoff based methods that you have consistently had good success with, and that it was reproducible?

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After trying WGCNA, or various correlation or mutual information based cutoff methods, I feel like unless there are 30-40 conditions/time points/data points/etc, just about any of the methods is as good as (and some times even worse than) a simple Spearman's Rank correlation. And unless the resulting coexpression cutoff is going to give you one of the "known" random networks (hierarchical, scale-free, etc.), an arbitrarily chosen high cutoff value (of say 0.85) is as good as anything else.

How do you get around these scenario? If you know a better network model to create using expression data only, besides coexpression network, I'd love to hear that too.

Thank you!