Question: How to compare coexpression networks from two conditions with different sample sizes
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gravatar for thind.amarinder
13 days ago by
Italy
thind.amarinder10 wrote:

I have gene expression data from two conditions having different sample sizes i.e 8 and 32. I want to build a correlation among all genes (for both conditions) for comparison. So, I used "psych" package from "R" which use t-test based statistics for p-adjusted values calculations.

Is this approach correct for the comparison of the networks (after taking significant p-adjust) between two experiments of different sample sizes?

or should I use another approach for p-adjusted calculations? If yes, what would you suggest?

ADD COMMENTlink modified 9 days ago • written 13 days ago by thind.amarinder10
1

Ciao / buonasera, what are you testing with your psyche t-test? I would create the correlation networks separately, and then compare (between the network for each condition) metrics such as vertex degree, hub score, community structure, etc. A simple pipeline, here: Network plot from expression data in R using igraph

ADD REPLYlink written 9 days ago by Kevin Blighe42k

Ciao I am not exactly comparing networks. but want to compare interactions of particular set of genes (of my interest) with others (if any), above certain threshold of correlation . whether interacting genes are mostly the same or different in both condition. since sample size is different between conditions, so can I directly compare or not? if, no How I can justify this comparison.

ADD REPLYlink modified 8 days ago • written 8 days ago by thind.amarinder10

You want to correlate certain genes in one condition with those in the other condition?

ADD REPLYlink written 8 days ago by Kevin Blighe42k

Yes, how can I go about this?

ADD REPLYlink written 8 days ago by thind.amarinder10

You can correlate together where the dimensions are the same, i.e., on the genes. So, probably:

cor(t(x), t(y))

My preferred way would still be to produce a correlation network using all genes and separately for each condition, and then compare your genes based on vertex degree, hub score, and community structure.

ADD REPLYlink written 8 days ago by Kevin Blighe42k

How do you calculate significance ( p-adjusted values) for correlation?

ADD REPLYlink written 7 days ago by thind.amarinder10
1

There is a cor.test() function, which functions in the same way as the cor() function. After you derive your p-values, you can then adjust for false discovery with p.adjust()

ADD REPLYlink written 7 days ago by Kevin Blighe42k

Thank you, it seems the way "psych" calculated p.adjust is similar.

ADD REPLYlink written 7 days ago by thind.amarinder10
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