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