Hi,

I'm working with time series RNA-seq data, containig 6 time points. There are no replicates and there is no control groupe. I'm interested to find differential expressed genes between the first timepoint (1/reference) compared to all the other timepoints. The time points can be grouped in 3 groups like this: condition A: 1,2  condition B: 3,4,5  condition C: 6 (but they are not replicates).

I first tried to do a multifactor design with the design (in order to do a likelihood ratio test later):

colData=data.frame(row.names=colnames(ei2),
                   timepoint=c("1", "2", "3", "4", "5", "6"),
                   condition=c("A", "A", "B", "B","B", "C"))

design(ddsMF)=formula(~condition+timepoint)

ddsMF=DESeq(ddsMF)

but it gives back the error:

Error in checkFullRank(modelMatrix) : 
  the model matrix is not full rank, so the model cannot be fit as specified.
  One or more variables or interaction terms in the design formula are linear
  combinations of the others and must be removed.

I don't know how to fix the problem with the model matrix neither if the approach is right. Are there any suggestions on how to solve this problem or antoher way to find differential expressed genes?

Thanks in advance!

DESeq2 requires replicates and so you cannot compare each time point to the first one, but you can code time as a numeric and use a smooth function to model the counts. Eg a design of f(time) using a smooth function ‘f’ such as splines or polynomials, with a likelihood ratio test comparing to a reduced design of ~1. You should contact a local statistician to assist with the modeling choices if you have not used splines or polynomial functions in linear models in R before.