R Limma P-Value vs Foldchange on cytokine data

I am trying to work with limma from Bioconductor to calculate the p-values and foldchange values and find differentially expressed genes.

My data looks like this.

 1. CYTO     Value  
ABC1       2.3   
ABC2    2.3   
ABC3    2.5   
...  
    PQR1    3.1  
 PQR2    3.2  
 PQR3    3.1

I want to use the limma package to first compute design = model.matrix(~0+group) and then fit <- lmFit(Data$VALUE , design) after which I can use the eBayes() function and calculate p-values and fold change values.

NOTE: We are trying to find which gene EG: ABC1, is more differentially expressed then another. 2: gene name is a combination gene_name and visit info eg: ABC1 (1 is the 1st visit)