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Learning datasets A and B were created by normalization (dataset A) and additional temporal smoothing of normalized values (dataset B).
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Normalization of imputed dataset: imputed dataset was normalized using quantile normalization in R (Bolstad et al., 2003 ) and logged to base 2. Array outlier detection: dissimilarity matrices of the normalized dataset were generated in AVADIS-Pride (Gwadry et al., 2005 ) to determine outlier arrays within the dataset.
For IDW method, after normalization of dataset, the power function was optimized and the cross-validation dataset was analyzed.
After the alignment and normalization, the dataset was composed of 190 samples represented by 7950 probes.
The new system, GNormPlus, achieved an F1-score of 0.867 on the BioCreative II gene normalization task dataset.
The interpretation of results from such experiments (i.e., the determination of differential expression for a particular gene among datasets) is strongly influenced by the selection of reference genes for normalization across datasets [ 1].
The quantile normalization makes datasets have identical distribution by averaging values at the same ranking in each dataset.
Despite the value of performing data normalization on datasets affected by within-plate effects, normalization methods often introduce bias when applied on any dataset (Dragiev et al., 2011).
Normalization between datasets was done with uniquely aligned spike-in RNA reads.
There may be more drastic variation in cultured cells or tumors, and we have also not extensively addressed normalization of datasets containing more than one tissue type.
However, these methods are not generalizable because of the lack of normalization across datasets ('t Hoen et al. 2013; Broman et al. 2015).
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