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Figure 1: MedianCD and SVCD normalization resulted in the detection of much larger between-condition variation in the datasets with differential gene expression, compared to Median and Quantile normalization.

In those cases, Median and Quantile normalization resulted in the smallest detected unbalances, whereas MedianCD and SVCD normalization yielded the largest ones, with values near for all but two treatments.

Both Quantile and Lowess normalizations resulted in loss of the Sensitivity on highest expression levels.

Some immediate manipulations result in equalities such as and.

Weight summation for normalization results in global memory accessing.

In fact, clustering without normalization results in a user separation that is solely based on values.

If normalization results in advanced tumor growth, rather than a window of opportunity, treatment strategies must avoid normalization as an endpoint.

Compared to MedianCD and SVCD normalization, the other normalization methods resulted in notably more severe degradation of the FDR.

Although the same DE tests were applied after data normalization, six different normalization methods result in varied TPR.

Figure 9: In the Platinum Spike dataset, all normalization methods resulted in similar detection of differential gene expression, with MedianCD and SVCD normalization being only marginally better.

It is noted that pitch-normalization results in 15% relative improvement over the baseline performance.