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Using the lumi package, we implemented various types of background correction (e.g. 'none'bgAdjust'st'forcePositiveivarianceriance stabilization ('vst' (variance-stabilizing transformation), 'log2', 'cubicRoot') and normalization.
variance stabilization transforms.
A second problem is that an accurate noise model is needed in order to build such a variance stabilization transform.
A first problem is the fact that a signal model that works fine in a linear domain may function poorly after variance stabilization [13].
The Haar-Fisz (H-F) transform used here is a member of a class of transformations known as variance stabilization transforms (VST) [18].
Percentage values were square root transformed for variance stabilization.
In the case of WF microscopy, one has to combine variance stabilization and bleaching correction.
The data were transformed using the variance stabilization transformation (VST) [37] and normalized using the robust spline normalization (RSN) [36].
Background correction, between-array normalization and transformation were carried out using the VSN method for variance stabilization and calibration of microarray data.
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However, different data sets may require different variance-stabilization transformations.
The data were normalized using a variance-stabilization procedure (VSN) [ 45].
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