Question: How to compare coexpression networks from two conditions with different sample sizes
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gravatar for Thind amarinder
18 months ago by
Thind amarinder100 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 18 months ago • written 18 months ago by Thind amarinder100
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 18 months ago by Kevin Blighe67k

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 18 months ago • written 18 months ago by Thind amarinder100

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

ADD REPLYlink written 18 months ago by Kevin Blighe67k

Yes, how can I go about this?

ADD REPLYlink written 18 months ago by Thind amarinder100

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 18 months ago by Kevin Blighe67k

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

ADD REPLYlink written 18 months ago by Thind amarinder100
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 18 months ago by Kevin Blighe67k

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

ADD REPLYlink written 18 months ago by Thind amarinder100

@kevin Blighe, could please give some more information on these vertex degree, hub score, and community structure? I am just doing correlation for two different conductions with the same number and type of genes and would like to understand how they can help me to compare my correlation matrixes Thank you!

ADD REPLYlink written 8 weeks ago by adR50
1

Hi, have you searched the igraph documentation?

ADD REPLYlink written 8 weeks ago by Kevin Blighe67k
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