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This normalization removed intensity dependent biases from each printing block in each slide.
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This removed intensity-dependent bias introduced by the use of the two different fluorophores as probe labels using the Loess (Lowess) method [ 19] and converted the data to a log ratio with the mean set to zero following normalisation.
Bias correction was performed to remove intensity non-uniformities.
Loess normalization was applied to each array to remove intensity dependent trends [ 37].
Then lowess normalization, intra- and inter-slide normalization were applied to remove intensity dependent trends.
After filtering and background correction, all arrays were normalized using loess to remove intensity dependent variations in the ratios [ 41].
For both reference and signal beams, a differential detector (DD) was used to remove intensity noise (Fig. 2b).
This pre-analysis normalization step removes intensity modulation due to varying concentrations of biological material and allows the same intensity thresholds to be applied across all data sets.
For each replicate slide, the mean and background intensity values from each channel (W595, W685) were log2 transformed and normalized using the LOWESS algorithm to remove intensity dependent effects within the calculated values.
Within Limma, minimum background correction and print-tip loess normalization was performed for each array to remove intensity-dependent dye bias and spatial effect.
We applied LOWESS normalization, a smoothing adjustment that removes intensity-dependent variation in dye bias [ 25].
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