Statistical analysis of differential gene expression relative to a fold change threshold on NanoString data of mouse odorant receptor genes

We developed a systematic statistical method, tTREAT, to identify differentially expressed genes with respect to a predefined FC threshold. The tTREAT approach aims to reduce false discoveries. We applied statistical tests relative to a Fold Change threshold to a dataset about mouse odorant receptor gene expression generated by the NanoString technology. We used mouse strains ∆P and ∆H that lack the P element and the H element respectively. We also used a mouse strain generated by chromosome engineering (∆Olfr7∆) whereby a 2.4 Mb region is deleted, thus lacking 99 odorant receptor genes. We sought to understand the regulation of the expression of odorant receptor genes by comparing mutant mice to wildtype mice.