Hello (Ciao?) Silvia,

The main doubt is why you put “weight” when you do the negative, positive colors correlations, what it means “weight”, what do?

'weight', in this context, relates to the Pearson correlation value between any two vertices (genes). This is due to the fact that the initial network was created from a distance matrix of Pearson correlation values, via:

as.matrix(as.dist(cor(t(estrogenMainEffects), method="pearson")))

In other situations, 'weight' may relate to Euclidean distances, or something else. Positive correlations are then encoded as red, while negative correlations are blue.

The second doubt I have is, what do the function(x)? And why do you put (x-min(x))/(max(x)-min(x)?

This function scales the vertex sizes to be between 0 and 1. It ultimately means that genes that have higher expression will have larger vertices. Here is an example:

x <- data.frame(a = c(1,2,3), b = c(4,5,6))
x
  a b
1 1 4
2 2 5
3 3 6

(x-min(x))/(max(x)-min(x))
    a   b
1 0.0 0.6
2 0.2 0.8
3 0.4 1.0

Finally, when I saw my graphics I observed that is a samples aggregation of my data, this code shows you the samples aggregation but if I want to see the genes aggregation of my samples what code/tutorial I should follow?

For this, you just need to transpose your input data, via t()