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There are several methods of normalizing the data and calculating the fold-change of each gene between samples.
In the process of establishing molecular equivalence, we test different methods of normalizing the data to remove institutional bias and we comment on their effectiveness.
In older adults, the variability in results may be due to statistical methods of normalizing for age and head size [ 11, 12].
We attempted several methods of normalizing serum or plasma miRNAs, such as spiking in exogenous miRNA as controls.
Creatinine kinetic modeling and back-calculating baseline creatinine from presumed GFR have evaluated AKI definitions [ 5, 8, 9], identified creatinine-based outcome metrics for clinical trials [ 10], quantified the effect of calculated baseline creatinine on AKI epidemiology [ 11- 13], and compared methods of normalizing urinary injury biomarkers [ 14, 15].
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This method of normalizing was extensively described elsewhere [41].
In this report, we provide evidence to demonstrate that the classical ΔF/F method of normalizing functional signal can introduce dynamically-changing biases in amplitude quantification of neural activity.
The method of normalizing the gene expression data is based on research in [ 58], in which the geometric mean of several internal controls was used to normalize the gene expression data.
We present a robust method of normalizing and analyzing ERs based on arbitrary thresholds and statistical criteria, and characterize a "norm of reaction" of ERs for 6 genes (4 of known function, 2 of unknown) with different ERs as determined across all analyzed arrays to provide a biologically-informed alternative to the use of arbitrary expression ratios in determining significance of expression.
The row permutation procedure adopted by the method has the objective of normalizing the enrichment score.
Here we report a method (designated the "Percellome" method) for normalizing the expression of mRNA values in biological samples.
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