Experimental Design for Differential expreression analysis I have a Normal esophageal Fibroblasts (NOFs) cultured in DMEM media; The same NOF also have been cultured with a tumor sample from a patient named 005 on DMEM media; I have also Cancer Associated Fibroblasts (CAFs) cultured on DMEM, on DMEM + tumor from a patient named as 005 and DMEM + tumor from a patient named 036 something like this picture;



I want to know if NOF and CAF are different in terms of gene expression but I am not sure about design for DESeq2 or Edger and ANOVA.

By @Devon's help I have done so

&gt; mycols
   cell_type co_cultured
G2       NOF          NO
G3       NOF         YES
G4       CAF          NO
G5       CAF         YES
G6       CAF         YES


dds=DESeqDataSetFromMatrix(countData = NOFCAF,colData = mycols, design        =~ cell_type*co_cultured) 


Now, I am wondering what would be a suitable contrast for results function in DESeq2

&gt; resultsNames(dds)
[1] "Intercept"                   "cell_type_NOF_vs_CAF"        "co_cultured_YES_vs_NO"      
[4] "cell_typeNOF.co_culturedYES"



&gt; res=results(dds)
&gt; res
log2 fold change (MLE): cell typeNOF.co culturedYES 
Wald test p-value: cell typeNOF.co culturedYES 
DataFrame with 2545 rows and 6 columns


Thanks a lot for correcting me
You're really running out of degrees of freedom and the only actual replicate is a single sample from another patient, so please take the results with a huge grain of salt.

You can use a design of ~cellType * coculture where cellType has levels NOF and CAF and coculture has levels yes and no (ideally you'd block by patient, but that's not an option for you).