This is a test version of Biostars. For the public version, visit https://www.biostars.org.
linear mixed model for gene expression data in R

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!

r
• 1,512 views • link
written by evafinegan • evafinegan

Thank you! Is it better than lme4 package?

• 0 views • link
written by evafinegan • evafinegan

It is probably the most widely used and accepted package for microarray and RNA-seq differential analysis for the last 15 or so years. Never heard of lme4 (not saying it is not good, I simply do not know it).

• 0 views • link
written by ATpoint ATpoint

Thank you! I do not want to do differential expression analysis. I want to run a linear mixed model to generate a value for each gene as shown in my original question.

• 0 views • link
written by evafinegan • evafinegan

0 answers

No answers yet.

Log in to answer this question.

More posts like this