While in many cases test gene expression was not detected in normal lung, a differential expression of the test gene of ⩾two-fold in the tumour was considered to indicate a relative over-representation of that gene in the tumour.
Dr. Vogelstein said he believed the technique could help many tests for flawed genes that promote diseases, although the amount of improvement would depend on the nature of the genes and mutations involved.
In order to deal with the multiple testing issues derived from the fact that many tests (one per gene) are performed simultaneously, p-values were adjusted to obtain strong control over the false discovery rate (FDR) using the Benjamini and Hochberg method.
Evidence for alternative mRNA polyadenylation sites was observed for many of the tested genes.
One statistical method for identifying genes that are differentially expressed is to perform many univariate analyses, testing genes one at a time for differential expression between groups, and then identify the genes which show the most statistically significant differences.
However, it is clear that the dramatic changes in gene expression in the predominant FFUF and FFDF microarray clusters were replicated by the qRT-PCR analysis for many of the tested genes.
Real-time quantitative PCR (QPCR) confirmed the RNA-seq results for many of the tested genes (Table 1), i.e. 18 out of 29 analyzed genes were replicated in independent samples at the mRNA level.
We noted that the expression of many of the tested genes modulated by MMF exposure were dependent on NF-κB activation, including Tnf, Il1b, Sp1, Hmox1, Nos2, and Nr4a2 [ 6, 25, 26, 31, 33, 37, 44, 60].
To determine significance levels, the RP method uses a permutation-based estimation procedure to transform the p-value into an e-value that addresses the multiple testing problem derived from testing many genes simultaneously.
The move to testing many genes in a 'panel' (also known as multi-gene or multiplex testing), or performing WGS/WES, increases the likelihood that a VUS will be detected in at least one gene because far more genes are being tested, and variants uncovered in other genes are more likely never to have been seen [ 3, 4, 36].
