The present mitochondrial microarray resequencing results are consistent with additional experimental verification from other investigators showing at least 99.99% microarray accuracy compared to sequencing [65], [69], [70], [71], [72], [73], [74], [75].
To examine microarray accuracy using real experimental data, we calculated the signal detection slope of microarray expression values versus log-transformed, control-gene-normalized nCounter measurements by fitting linear regressions to the paired platform measurements of each gene.
Gene-specific quantitative qRT-PCR was used for the cross-validation of platform performance and as an assessment of microarray accuracy.
Though not described further here, the BFRM statistical model analysis used by the SFPA also addresses issues of gene-sample-study specific effects within the analysis and is able to correct enough of the idiosyncracies and bias inherent in microarray assays to retain predictive accuracy [19], [31].
This is crucial for the accuracy of the microarray results to determine significant differences in gene expression between treatment and control groups [ 4].
Putative inducible genes were compared with genes upregulated more than twofold on a microarray to check their accuracy.
The expression level of these 2 genes and 3 markers above was examined by sqRT-PCR to validate the microarray data accuracy (Additional file 1: Figure S2b).
We showed for the first time that expression values of only five miRNAs, measured by quantitative PCR, are able to classify the dignity or entity of the RCC samples with an accuracy comparable to microarray-based techniques.
The resequencing microarray's accuracy for determining the base call has been established under a variety of conditions [ 23].
One metric used for determining microarray measurement accuracy is the "signal detection slope" [ 5], or the slope of linear regression relating measured microarray expression values to log-transformed, known input transcript concentrations.
