Hi all,

I have data from a RNA-seq experiment where 90 individuals were sequenced. Three biopsies were taken from each patient at different locations, i.e, for the same individual we have three different RNA-seq samples corresponding to three different locations (proximal, distal, rectum). We are interested in see which are the differences between locations.

I thought of using a blocking factor with duplicateCorrelation to account for the fact that each patient has 3 different locations.

An example of the metadata and of the steps I'm thinking of using:

head(targets)
  Sample_Name  Location    sex  age  patient_ID
1        2941  Proximal   Male  68          294
2        2942    Distal   Male  68          294
3        2943    Rectum   Male  66          294
4        1331  Proximal Female  24          133
5        1332    Distal Female  24          133
6        1333    Rectum Female  24          133

location <- as.factor(targets$location)
sex <- as.factor(targets$sex)
age <- targets$age

#dataset is the expression data object

design <- model.matrix(0~Location+sex+age)
corfit <- duplicateCorrelation(dataset, design, block=targets$patient_ID)

fit <- lmFit(dataset, design, block = targets$patient_ID, correlation = corfit$consensus)
efit <- eBayes(fit, robust=TRUE)

Are these steps appropriate to account for patient effect?

Thanks

The usual design matrix for this sort of experiment is

mode.matrix(0 ~ Location + patient_ID)

There's no need to adjust for age or sex. There's no gain to be had from duplicateCorrelation unless you don't have all three biopsies from all the patients.