Hi,

My previous post is displayed first.
*> 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.
*

The dataset in brief is as follows:


                     A               B
C          D                G        H              I
650349  12.969875 13.242497 13.326932 13.351698 13.351698 13.264294
13.210808
2510494  9.042255  9.167198  9.131402  9.272791  9.371630  8.968158
8.902732
2940041  7.010943  7.042466  6.928938  7.084090  7.284017  7.498544
7.091544
3830112  7.520437  7.499940  7.385284  7.428067  7.217722  7.356394
7.021526
6560392  7.685423  7.576733  7.692961  7.616913  7.597422  7.784396
7.750956
1450041  6.595077  6.532918  6.461785  6.372699  6.395522  6.675855
6.487440

I will try and get a reproducible version too. Just to explain:

I have 2 patients, with around 20,000 probes each. Each patient has
observations of gene expression (response) taken from some regions of their
tissue. Then the next level in the hierarchy of the data is the sub region
- we have data from several subregions for each region of each patient. So
I have 3 levels of nesting : subregion within region within patient.

Here, A, B, C and D correspond to subregions of patient 1 and together they
comprise Region 1 for the first patient. GHIJ comprise the next and so on.
In such a situation, is the following the correct syntax for the lmer
command?
( I am considering patient, region and subregion to be random factors. )

*fit <- lmer(expression ~ 1  + probe + (1|patient)+ (1|patient:region) +
(1|patient: region: subregion) )*

The problem is that this encounters the same problem as lme. The whole data
is of the dimension 20,000 x 22, where 20,000 is the number of probes and
22 is the number of samples/arrays.

Any advice would be very helpful!

Cheers,
Angelina

>
>

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