Hi all,

I was wondering if you could help me to understand limma linear regression output for continuous variables.

I got result for epic array methylation data and eGFR association in this format;

```
logFC AveExpr. t P.Value adj.P.Val B
cg03546163 0.41018776 -0.8013218 7.85104192 1.13E-12 8.65E-07 12.3051291
cg17944885 -0.4884859 2.31974279 -5.7319645 6.33E-08 0.02431349 5.15237109
```

Could you please help me to understand what is difference between B, logFC, t, and AveExpr. and what are more important output need to consider to for eGFR and CpG association here?

I have used this below model at adjust for covariates;

```
#model matrix
var<-model.matrix(~logeGFR + as.factor(Gender) + Age +CD8T +CD4T +NK + Bcell +Mono ,data=targets2)
```

Thanks again for your all help. Please share any more documents or papers that can help to understand it more.

Many thanks

Thanks James for help and time.

I have converted eGFR into log value by using below equation and used M value for methylation data.

Could you also help me to understand other output options like what is B and t and AveExpr here? Do they have any significance while reporting results? Thanks again!

Ideally you would use log base 2, as it's more easily interpreted.

All the columns are described in detail in the help page for

`topTable`

, so you can consult that. I only provided interpretation for logFC because the interpretation is different for a continuous covariate than for a factor (which is how it's described in the help page, because that's like 99% of the use-cases).As well as the topTable() help page, another document is Chapter 13 of the limma User's Guide.

Thank you so much for all help.

Could anyone please help me understand the negative association between methylation and eGFR? Additionally, I'd like to know how to identify the CpG sites where methylation increased while eGFR values decreased, as well as the reverse scenario.

Thanks again!