29 During the last two decades, multiple statistical approaches have been proposed for differential expression testing.
We have collected replicates for both D. yakuba and D. simulans, contributing to the greater power in differential expression testing.
Cufflinks assemblies from all samples were merged using Cuffmerge and Cuffdiff was employed for differential expression testing.
The effective reduction of candidates by differential expression testing after adjustment was 63.2%, 69.5%, and 59.8%, respectively, i.e., 64% on average.
We have sequenced samples to extremely high coverage, generating transcript annotations with 5′ and 3′ UTRs with empirically supported intron-exon structures, improving accuracy in differential expression testing.
These represent 29%, 22.6%, and 33.2% of genes in the chromosomes 2, 11, and 17 donor regions, which were excluded from differential expression testing.
Furthermore, lack of genome coverage from the microarray used and removal of probes overlapping SNP excluded ~30% of positional candidates from differential expression testing.
However, the lack of consistent statistically significant enrichment for the smoker phenotype with GSEA analysis taken with the degradation in RNA derived from buccal cells highlight the difficulties to be expected when using buccal-cell RNA for differential expression testing.
Differential expression testing reduced the number of positional candidates from 1596, 1132, and 1347 to 124, 89, and 95 genes, that is, a reduction in 92.2%, 92.1%, and 92.9%, for HG2D, HG11, and HG17, respectively.
That is, the ranking order of betweenness scores was almost completely uncorrelated with the results of differential expression test, and the differentially expressed tests only provide a low accuracy for the prioritization of genes.
