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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;

enter image description here

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

> 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

> resultsNames(dds)
[1] "Intercept"                   "cell_type_NOF_vs_CAF"        "co_cultured_YES_vs_NO"      
[4] "cell_typeNOF.co_culturedYES"



> res=results(dds)
> 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

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    $\begingroup$ Do you have replicates of any of these? $\endgroup$
    – Devon Ryan
    Jan 7, 2019 at 4:53
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    $\begingroup$ Following Devon comment, you don't need biological replicates, technical replicates are worthy. It would help to know how many samples do you have available of each type. In addition, it is important to consider potential batch effects (where all samples extracted and sequenced at once?) and con-founders (are some sample from older patients? Is sex equally distributed) $\endgroup$
    – llrs
    Jan 7, 2019 at 8:18
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    $\begingroup$ No, you really need biological replicates. The conclusions you can draw from technical replicates only is limited. $\endgroup$
    – swbarnes2
    Jan 7, 2019 at 17:11
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    $\begingroup$ You can figure out that error from DESeqDataSetFromMatrix yourself, you shouldn't need any help (hint: design is stated incorrectly). $\endgroup$
    – Devon Ryan
    Jan 8, 2019 at 11:22
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    $\begingroup$ You don't require any further help on this, you need to fly free a bit more. $\endgroup$
    – Devon Ryan
    Jan 8, 2019 at 12:19

1 Answer 1

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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).

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  • $\begingroup$ Sorry, @Devon, this design would return the interaction of NOF and CAF with co_culture or returns gene different between NOF and CAF by considering the interaction of cell_type and co_culture? Thank you for assigning your time to me $\endgroup$
    – Angel
    Jan 8, 2019 at 11:45
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    $\begingroup$ No, this design fits the interaction as well as the main effects, what coefficient you extract is up to you. $\endgroup$
    – Devon Ryan
    Jan 8, 2019 at 12:18

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