How do you compare unpaired biological replicates?

Question

I have done proteomics and I have obtained genes with ratios. As an small example you can see my data below

Gene    Control1     Control2   Control3    Treated1    Treated2    Treated3
pps-1   324680000   211350000   356350000   269770000   258080000   292830000
R11A8.7 477490000   610780000   539550000   533590000   530810000   578290000
ugt-21  105080000   103430000   74137000    78915000    42381000    31415000
spp-18  1042800000  615030000   332720000   538340000   448280000   412310000

Now My question is that I have Three controls and Three Treated, Control has two biological replicate and Treated has two biological replicate

I am trying to find the pathways which the list of my genes activate it. I use IPA which is a tool for pathway analysis

How can I calculate the fold change for it? If I use the 6 values, IPA will give me 6 different results and then interpretation will lead me to hell.

I appreciate any help.

Answer 1

Calculating fold change is pretty straightforward but you cannot do one on one comparison because they (control and treated) are not really paired (same sample). That's why IPA gives you six results (FC for each pair). However, you can calculate fold change between two genes because they are from the same sample. Note that fold changes are quite dangerous and can lead to misinterpretation of data (for example 1/2 would be same as 100/200). You should use them cautiously.

I think, in this case, you can simply apply a t-test. This will tell you if your two groups i.e. treated and control are significantly different from each other. You would not be calculating fold changes here. If you had more number of groups then you can go for one-way ANOVA.

I am not sure what output you really want after the analysis but I think the main goal is to say that control is different from treated.

Since you have multiple genes (variables) you can either go for a multivariate t-test, which will tell you if the control is overall different from treated, or many univariate tests which will tell you how each gene differs between the two groups. There are some methods such as Bonferroni correction which can be used to improve the prediction of multivariate tests.

When you have established that a certain gene is differentially expressed, using t-test, you can report the fold change of the averages.

Answer 2

While I do not have any experience with IPA, I routinely face this problem.

How do you analyse separate biological replicates?

One way to approach the problem is to merge the biological replicates.

There are several methods,

To do this, you can show a pearson or spearman correlation. If you have experience in R, it is a simple task of applying the function cor.

I do not use excel for such tasks, but you can check out this excel tutorial which covers correlation in Excel.

The second and harder method is to show the clustering of the replicates, using one of the many clustering methods out there. For this the easiest way I know is heatmap2 from the gplots package, which uses hclust for hierarchical clustering. This method will produce a heatmap with a dendogram specifying the relation between the replicates. Check the package link for the pdf containing details on the heatmap.2 function and how to use it.

I would go with correlation since it hardly takes 10 minutes. Having shown the correlation between replicates, you can then merge the replicates by taking the average of the replicates.

I do not know if IPA has a way for dealing with replicates