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Based on these normalized response plans, the Pareto-efficient optimization searches for the best compromise in all dimensions by searching for a set of response plans where all evaluations are close to the current-best response plan in that dimension.
Averages and standard deviations between experiments were then calculated based on these normalized values.
Unless otherwise noted, all analyses were based on these normalized profiles.
Similar to probe design, we used two empirical thresholds R L and R U to classify probes based on these normalized hybridization data.
Based on these normalized read counts (NRC) expression patterns of different LTR REs were analyzed for each transcriptome (Additional file 1: Tables S1 and S2).
The original sequence counts of miRNAs were normalized to RPM (reads per million), and miRNA expression analysis was performed based on these normalized data at the miRNA and isomiR level.
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In Additional file 5, Table S3, the standard deviation for each of these genes, based on the normalized signals, is shown.
Next, the genes are clustered based on the normalized regression coefficients under these cultivation parameters.
From these loadings, we created weights based on the normalized square of the loading.
Additionally, based on the normalized OTUs, alpha diversity was applied to analyse the complexity of species diversity for the samples.
Active bandpass filter designs are based on the normalized lowpass pole and zero locations.
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