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The intensities of signals from features within each array were normalized by print-tip Lowess normalization [ 21, 22].
Copy number estimates (log2ratios) for each array were normalized [ 26] and replicated samples were merged after normalization.
The miR expression values from the array were normalized to the mean expression level of all miRs in the respective sample, to adjust for the different quality of RNA preservation and extraction.
Collected data from the tiling array were normalized with the median correction algorithm.
For comparison across different arrays, the data for each array were normalized by Robust Multi-chip Average (RMA) [75] using Spotfire DecisionSite (Spotfire, Inc., Somerville, MA) and GeneSifter (VizX Labs, Seattle, WA), and probe sets with expression values below the level of background noise (as determined by detection p value) were disregarded in further analyses.
Data for each array were normalized using the median for the entire array.
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Each array was normalized to an array of median brightness, using local regression on an invariant set of probes [78].
The average intensity on each array was normalized by global scaling to a target intensity of 1000.
Because each antibody performs differently with respect to binding specificity on the array, the SNR for each array was normalized to a range of 0 100.
Probe intensity data for each array was normalized to a baseline array with median signal intensity using the "invariant set" model.
Each array was normalized by subtracting the sample-median value.
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