**130**wrote:

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

Question: How to find p-value of the modules in WGCNA?

2

fernardo • **130** wrote:

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

6

Kevin Blighe ♦ **63k** wrote:

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

Thank you very much Kevin.

I could manage to find the matrix you mentioned between the samples and the modules. But what could be the "**Weight**" or "**CaseControl**"?

Thanks

1

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()))
```

Thanks. Understood. Even, unfortunately, it gave an error while I was running CaseControl;y (`summary(glm(y ~ Module1, , data=MyData, family=binomial()))`

):

```
Error in eval(expr, envir, enclos) : y values must be 0 <= y <= 1
```

You will have to encode `MyData$y`

as a categorical variable via the factor() command. For example:

```
MyData$y <- factor(MyData$y, levels=c("Control", "Case"))
```

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

1

Well, we now know that *MEblue* has nothing in relation to *CaseControl*! - p-value=0.998.

You should now check each module independently and see which are statistically significant.

The warning message most likely appeared as a result of low sample numbers.

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.

Is the blue module also the module with the highest number of genes?

You also did the correlation analysis of the modules to your phenotypes? - were the results the same as the regression?

Not actually. The blue module has not the lowest or the highest number of genes (DEGs).

What correlation do you mean? Do you mean using "`summary(lm(CaseControl ~ MEblue))`

"? Yes I tried this and were highly significant for almost all the modules.

But, if the blue module is not related to CaseControl, then what is your interpretation of that? It could represent the 'common' pathways across all of your samples. The interesting modules should be the ones that are statisatically significant to CaseControl.

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?

Yeh, but, even looking at the values for turquoise, there are no great differences, and the sample *n* is too small. The correlation test worked how?

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)
```

1

I thought that you had 6 samples, but I was mis-reading your terminal output. You could check the distribution of the values in that module, which may reveal more information.

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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