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Microarray data were normalized using median normalization method.
Gene expression data for all the groups were normalized using median normalization method.
cDNA data were normalized using the median normalization method and Affymetrix.CEL file data were processed using the Robust Multi-Array Average (RMA) algorithm [31].
MicroRNA arrays were analyzed using GeneSpring GX v11.5 (Agilent Technologies, CA, USA) with the standard normalization method for one-channel microarrays with the percentile median normalization method.
For an individual class, the median normalization method was generally better than the control normalization method with the exception of the control class whose prediction accuracy was higher using control normalization than median normalization.
We used Median Normalization Method to obtain "Normalized Data," Normalized Data = (Foreground − Background)/median; the median was 50percentt quantile of microRNA intensity which was larger than 30 in all samples after background correction.
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We have investigated several normalization methods, including quantile, mean, and median normalization methods, and endogenous controls identified using various stability criteria.
We detail in the Methods section (subsection Median Ratio Normalization Method) the interest of such an assumption.
Median-based normalization methods [43, 65 68] are also widely used.
Two normalization methods were applied, median normalization and control normalization.
In brief, background-subtracted intensities were calculated using foreground and background median signals, and normalized with the quantile normalization method.
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median mark method
median power method
median filter method
median expression method
median imputation method
median polishing method
median normalization factor
median split method
median scaling method
median filtering method
median t method
median polish method
median normalization algorithm
median effect method
median rank method
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