Due to this greater accuracy, expression-based classifications offer an opportunity to improve molecular classification of gliomas [ 6, 7] and clinical diagnosis of glioblastomas [ 2].
Although the accuracy of expression levels was unclear based only on the clone numbers of the EST database, the relatively high expression of the 30 most highly expressed genes of P. xuthus was subsequently confirmed by microarray analysis.
What and how many reference genes used for calculation of normalization factors (NF) in parallel samples is thus a crucial determinant of the accuracy of expression quantification.
The maximum predictive accuracy of expression for pentachlorophenol is 69% considering the 500 most predictive transcripts, while the maximum predictive power of genotype for it is 90% considering the single most linked genetic marker.
For more accuracy, the expression study was conducted using three different dilutions of cDNA.
To evaluate the computational accuracy, the expression levels of four known miRNAs were experimentally validated.
We apply our algorithm to simulated and biological datasets, demonstrating a significant increase in speed with only very small loss in accuracy of expression level estimation.
However, for a fixed sensitivity, the precision of the annotations does not have a discernible effect on the accuracy of expression estimates.
We explored the uniformity of RSPDs in RNA-Seq data and analyzed how using a non-uniform model affects the accuracy of expression estimates.
We present evidence demonstrating that hybridization time can in fact increase the accuracy of expression ratios (fold changes), relieving the observed compression in ratios, and that this effect may be more dramatic for larger fold changes.
To assess accuracy, gene expression estimates obtained from RNA-Seq data were compared against those obtained from TaqMan qRT-PCR measurements (GEO accession GPL4097) collected as part of the MAQC project.
