Hi,


I have gene expression data and have a subset of the same, comprising
about 20,000 rows (genes) and 3 factors. I am interested in how these
factors like patient affect the gene expression. An outline of my data
frame is shown below:



   Probe  patient region subregion expression
1  650349    1      1         1    12.969875
2 2510494    1      1         1    9.042255
3 2940041    1      1         1    7.010943
4 3830112    1      1         1    7.520437
5 6560392    1      1         1    7.685423
6 1450041    1      1         1    6.595077


I fit the following: *> fit <- lme(expression ~ Probe + region +
subregion , random=~1|patient/region/subregion, data=df)*


I get the following error:*Error in model.matrix.default(fixed, data = X) :
  allocMatrix: too many elements specified*


Here I am investigating whether different subregions within multiple
different regions nested within various patients affect gene
expression response. I cannot model the response per *probe* as I'd
have only one observation then.


Any suggestions would be deeply appreciated.


Kind regards,

Angelina

	[[alternative HTML version deleted]]

