Hi,
I did run WGCNA package successfully but I couldn't find a way or line of code to get p-value of the modules. I have seen it in a paper that there p-value were given to the modules. Can you help please?
Thanks a lot
Buonasera Fernardo,
The idea is that you, first, derive your modules via the standard WGCNA functions and, second, obtain the module 'scores' for each sample to each module. This will give you something like:
Module1 Module2 ... ModuleX
Sample1 0.66 0.43 ... 0.33
Sample2 0.45 0.34 ... 0.2
... ... ... ... ...
SampleX 0.9 0.7 ... 0.45
Poi (then), you can use these module scores in regression analysis or in correlation, i.e., regressing the module scores to a clinical phenotype:
Any of the modules related to weight?
summary(lm(weight ~ Module1))
summary(lm(weight ~ Module2))
Any module relate to case / control status (binary phenotype)?
summary(glm(CaseControl ~ Module1, family=binomial()))
summary(glm(CaseControl ~ Module2, family=binomial()))
You can also build a simple correlation plot, like I have done here: CorLevelPlot - Visualise correlation results, e.g., clinical parameter correlations
Nota bene - WGCNA can also generate similar plot to this
Ci vediamo dopo,
Kevin
They would be 'phenotype' parameters, with 1 value per sample.
For example:
head(MyData)
Weight.Kg CaseControl Module1 Module2 ... ModuleX
Sample1 45 Case 0.66 0.43 ... 0.33
Sample2 47 Case 0.45 0.34 ... 0.2
... ... ... ... ... ... ...
SampleX 74 Control 0.9 0.7 ... 0.45
summary(lm(weight ~ Module1, data=MyData))
summary(lm(weight ~ Module2, data=MyData))
summary(glm(CaseControl ~ Module1, , data=MyData, family=binomial()))
summary(glm(CaseControl ~ Module2, , data=MyData, family=binomial()))
I changed Y(CaseControl) column to 0 and 1 and worked. But gave a warning message but maybe I can ignore it :)
Call:
glm(formula = y ~ MEblue, family = binomial(), data = xx)
Deviance Residuals:
Min 1Q Median 3Q Max
-8.106e-05 -2.100e-08 2.100e-08 2.100e-08 8.197e-05
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 190.1 72757.4 0.003 0.998
MEblue 1732.4 660316.6 0.003 0.998
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 3.4162e+01 on 28 degrees of freedom
Residual deviance: 1.3289e-08 on 27 degrees of freedom
AIC: 4
Number of Fisher Scoring iterations: 25
Warning messages:
1: glm.fit: algorithm did not converge
2: glm.fit: fitted probabilities numerically 0 or 1 occurred
Thank you very much, now I have all the answers. But if I may ask about one more interesting point, the blue module has more significant enriched pathways and ontology annotations with even more connection in PPI compared to other modules with low p-value but no enrichment or very less? I can't see it fair to go with other modules but not the Blue.
What do you say? your experience? Thanks a lot.
well I'd say that I received different clusters (modules) from WGCNA and one of them seems to be more significant based on pathways and so on, not based on the correlation we discussed above. And the genes are all differentially expressed between case/control, so the common pathways across e.g. the cases are something to be considered significant, right? why common pathways across the Case samples are shouldn't be relevant? What do you think?
Interestingly, in my case I observed the module that was the only significant module in correlation test (also the largest), failed in the glm fit test. Another module came out as significant which I was not expecting. Also the test with the largest module showed some warning as mentioned by @fernardo. Am I missing something in this?
the sample n is too small.
n=45, it that still small? What would be the minimal number of samples that I should target? WGCNA tutorial mentions at least 20 samples. [In image, only top samples displayed by head()]
For correlation test, I used the default Pearson's correlation:
moduleTraitCor <- cor(MEs, trait, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
The most observable thing that I saw for the MEs was that in turquoise module, the type1 samples had positive MEs (~ 0.14 or 0.06) while the type0 samples had negative MEs (~ -0.16 or -0.07 ). For other modules, no such uniformity were found.
Also, for the turquoise module the correlation was high in the scatter plot of gene module membership and gene trait significance. But as mentioned earlier this module did not qualify the glm() module trait association.
Dear Dr. Blighe I construct my co-express network but I don't know how can I calculate module 'scores' for each sample to each module. In other words, I can't manage the below table that you suggested in the last comment:
Module1 Module2 ... ModuleX Sample1 0.66 0.43 ... 0.33 Sample2 0.45 0.34 ... 0.2 ... ... ... ... ... SampleX 0.9 0.7 ... 0.45
I appreciate if you share your comment with me and guide me on how to get that table in WGCNA?
Dear Dr. Blighe I construct my co-express network but I don't know how can I calculate module 'scores' for each sample to each module. In other words, I can't manage the below table that you suggested in the last comment:
Module1 Module2 ... ModuleX
Sample1 0.66 0.43 ... 0.33
Sample2 0.45 0.34 ... 0.2
... ... ... ... ...
SampleX 0.9 0.7 ... 0.45
I appreciate if you share your comment with me and guide me on how to get that table in WGCNA?
You are searching for 'module eigengenes'. There is an example of what you need, here: https://www.polarmicrobes.org/weighted-gene-correlation-network-analysis-wgcna-applied-to-microbial-communities/
Search for moduleEigengenes(datExpr0, moduleColors)$eigengenes
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How to find the results for each modules (summary) in WGCNA? can you help me with code?
'Cross'-posted to here: A: WGCNA analysis - use only linkage coefficients for results analysis