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Such normalizations are indeed very important, especially when the expressions of several genes have to be compared across numerous samples.
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First, it can be compared across hemispheres.
They can therefore be compared across images and across individuals.
Efficiency of learning was compared across odorants.
Results were compared across technicians and across laboratories.
Scores were compared across cohorts.
Data was compared across participants.
Data were compared across specialties and geography.
Ratios between activities were compared across species.
Active genes were compared across genotypes.
Performance variables were compared across glucose ranges.
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