linear mixed model for gene expression data in R

Question

[evafinegan]

I have gene expression data for multiple genotypes as FPKM matrix:

gene    Sample1 Sample2 Sample3
gene A  19.6    13.5     16.7
gene B  17.8     19.7     14.3

In the matrix, genes are in rows, columns have samples with FPKM values for each gene across the samples. I want to run a linear mixed effect model using lme4 to correct this gene expression data for experimental effects. I want to fit separate models to each of the genes with response as FPKM, with an intercept, fixed and random effects of experiment from genotype, row and column within the experiment.

Previously I have ran basic straightforward models such as lm~x+y. But I have no experience in running linear mixed models using loops or arrays. I will appreciate any help on to run a linear mixed model on my data. Thank you!