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(c) Hardware design flow graph of the data normalization algorithm in PVs pre-processor unit.
The downloading of the data, normalization of the data, and the sentence to transaction translation was conducted by python script.
A detailed description of the data normalization procedure has been published previously (Rajapakse et al. 2004).
One crucial point in this study was the choice of the data normalization procedure.
See Additional file 1 for a description of the data normalization procedures and statistical analyses used to generate gene sets and/or ranked gene lists for each of the previously published gene-expression datasets.
Judging from the description of the data normalization, this spike-in control might however have been used sub-optimally; the authors state that they normalize their data based on the number of reads, which can only ensure that the same number of reads (i.e. the same 'sequencing power') goes into the analysis of each sample.
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The characteristics to be unified, the standards, and the set of calculations are prescribed by an intellectual framework, the basis of which is the data normalization scheme.
The successful removal of DEGs in the data normalization step generally increases both the sensitivity and specificity of the subsequent differential expression analysis.
Since inputting raw data reduces the accuracy and speed of the network, data normalization method, which prevents the excessive reduction of the weights and early saturation of the neurons, is used.
The sample data was analyzed by subtraction of the reference data, normalization to the protein concentration and DSC cell volume, and interpolation of a cubic baseline.
Due to the non-linearity of the data Lowess normalization was applied.
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