Dear all,

I have been trying many things now, but I think there must be an easier way for my quite simple problem.

My colData has Sample.Site = Heart, Liver, ...

Now for my analysis I just want to compare Sample.Site=Heart between Males and femals. How can I filter the colData in a way that I only have Sample.Site=Heart?

Thank you so much for any idea, Bine

The SummarizedExperiment class works just like a data.frame or matrix when you use the [ operator. So you would do just what one would expect. Using the output from ?SummarizedExperiment

> colData(se)
DataFrame with 6 rows and 1 column
    Treatment
  <character>
A        ChIP
B       Input
C        ChIP
D       Input
E        ChIP
F       Input

> se_sub <- se[,colData(se)$Treatment == "Input"]
> se_sub
class: RangedSummarizedExperiment 
dim: 200 3 
metadata(0):
assays(1): counts
rownames: NULL
rowData names(1): feature_id
colnames(3): B D F
colData names(1): Treatment
> colData(se_sub)
DataFrame with 3 rows and 1 column
    Treatment
  <character>
B       Input
D       Input
F       Input

You can also make different assumptions, such as assuming (or, like, checking) that the within-group variability is pretty consistent across groups, and just fitting the model in such a way that you can make the between-sex comparisons for heart using all your data. Or alternatively, you can specify a design that stratifies your model fit internally, using the / operator, so something like

design <- model.matrix(~Sample.Site/sex + othercovariates - 1, colData(se))

So an explicit stratification of your data is not always necessary or desirable.