Hola Carlos,

Instead of doing simple correlation, you could model the relationship between each epigenetic signal and the expression of genes surrounding the signal. What do I mean by 'model'? I mean build a linear regression model, as follows:

```
lm(NearbyGene1 ~ mark1H3k27me2)
lm(NearbyGene2 ~ mark1H3k27me2)
lm(NearbyGene3 ~ mark1H3k27me2)
lm(NearbyGene4 ~ mark1H3k27me2)
...
lm(NearbyGene1 ~ mark2H3k27me2)
lm(NearbyGene2 ~ mark2H3k27me2)
...
lm(NearbyGene1 ~ mark3H3k27me2)
lm(NearbyGene2 ~ mark4H3k27me2)
```

You will have to set this up as a loop. To use model formulae in a loop, you can create the model equation with `paste()`

and then coerce it into a formula acceptable to the `lm()`

function with `as.formula()`

.

To extract information from a model, use the `summary()`

function - there are ways of extracting each individual value via subsetting.

The benefit of using a model is that you can also adjust for other covariates / confounding factors, for example:

```
lm(NearbyGene1 ~ m2H3k27me2 + TissueType)
```

Take a look here for other information related to linear regression models (and there's tonnes of information across the World Wide Web, too): A: Resources for gene signature creation

Kevin