Hi everyone

I am new to all the gene expression and its different approaches to analyze. I am a little bit overwelmed with the information quantity, and the different alternatives My data is based on Ct values (cycle threshold) normalized to 3 reference genes (average of them). After, we calculate the 2^dCt value between the pre and the post obtaining a number generically known as fold change, and this way is known as relative quantification method.

I have seen that clusterProfiler as a package is useful analyzing fold change, but usually from RNA-Seq data. My question is which approach would you find more suitable to cluster the fold change obtained in my genes (PCA, k-means, clusterProfiler..) in order to plot the correspondent gene expression clusters

I am working within an experiment counting on 450 samples (50% pre and 50% post) in which we ran 55 genes (including 3 endogenous controls), and 3 groups (treatment1, treatment2 and control)

# include your problematic code here with any corresponding output 
# please also include the results of running the following in an R session 

sessionInfo( )