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The data were normalized using the percentile shift method to the 80th percentile.
The raw data were first normalized using the percentile shift 75 normalization method.
The raw signals were log transformed and normalized using the Percentile shift normalization method, the value was set at 75th percentile.
Raw signals were thresholded to 1.0 and an additional normalisation using the percentile shift algorithm to the 75th percentile was used.
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It was not surprising that comparisons made using the percentile categories of RDD distribution shifted the elevated risks from women whose exposures were above the 75th percentile (in the prior analysis) to women whose exposures were above the 90th percentile (in the current reanalysis).
90% bootstrap confidence intervals were computed using the percentile method.
We created 95% confidence limit intervals using the percentile bootstrap.
Data were then further normalised using the median percentile shift to 2 microRNAs that were identified to be unregulated across samples using NormFinder [32].
Extracted data were transferred into Microsoft Excel (Microsoft Corporation, Reading, UK) where all data were transformed to Log base 2. Transformed data were then corrected within each chip using the median percentile shift to 5 abundantly expressed positive control probes on each array.
Normalization of the data was carried out in GeneSpring GX by using the 75th percentile shift.
Because gene coordination relies on multiple samples to form a single measure, we used the percentile bootstrap method to ensure that the shifts were robust and significant (p<0.05) and then applied the Benjamini-Hochberg FDR [38]. Benjamini-Hochberg FDR [38]
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