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PCA is an unsupervised data transformation procedure of complex data sets.
A non-linear data transformation procedure subsequently converts the calculated environmental flow duration curve into a continuous time series of environmental flows.
Our generic data transformation procedure is streamlined for unbiased, explorative research.
Our generic data transformation procedure is built on a decomposition model for the double-mutant fitness matrix.
Three large-scale quantitative genetic interaction datasets on budding yeast (Saccharomyces cerevisiae), as available from different screening technologies, were used to demonstrate the performance of our computational data transformation procedure.
The advantages of the data transformation procedure are further confirmed using two published large-scale quantitative interaction datasets; one obtained with the GIM screening approach and the other with the E-MAP approach (Table 1).
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Section 'Methods' presents the data used in this study, together with data transformation procedures and statistical methods employed.
It is also possible that the candidates proposed by RefGenes are biased by the inherent nature of microarray data as compared to RT-qPCR data, or by data transformation procedures during normalization.
In order to remove systemic bias and random variation within and between different experiments, we conducted a series of data transformation and normalization procedures.
Following the log data transformation, an automatic search procedure for model selection based on the AIC criterion was performed.
Because only a log transformation was used originally, the independent analysis also investigated the use of untransformed data or a square-root transformation procedure.
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