Dear list,


I am a postdoc in Bioinformatics, working on gene/gene regulation using RNA-seq data. I would like to find the associations for a set of gene pairs that my collaborator sent me. I have 1000 such pairs whose counts are measured for 400 samples. One way to do it would be by simple correlations (Spearman CPMs) or by using limma (faster than edgeR and DESeq for this task) and model the voom-transformed data as Gene1 ~ Gene2. 


The problem I see with the 'correlations' solution is that it's a very simple model that does not take into account the dispersion of the data, while 'lima' or edger or other would possibly give different answers for Gene1 ~ Gene2 and Gene2 ~ Gene1, so it would be confusing if I wanted to estimate a bootstrap P-value of significance. 



I would like to ask if there is any model that uses orthogonal regression for RNA-seq data (assuming that all measurements come with error and that the error variances are equal).


Thank you,
Pan 		 	   		  
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