Namely, the transaction-normalized tables should be transformed before such methods could be applied, and some previous works have pointed out that such tasks of performing data transformation usually consume a lot of resources.
Given the large scale of values for metal concentrations (from unit to thousand), to standardize the data, we performed logarithmic data transformation.
Sample clustering was performed after data transformation via variance stabilizing transformation function (DESeq package; Anders and Huber 2010), and heatmaps were generated via the heatmap.2 function as available in the 'gplots' Bioconductor package.
The following normalization steps were sequentially performed: (1) Data transformation: set measurements less than 0.01 to 0.01; (2) Per chip: normalize to a set of pre-determined genes, using all 15,820 P. sojae genes as control genes; (3) Per gene: normalize to median.
The synthesis statements remain consistent with the review authors' work but also build in the results of the data transformation performed in earlier steps.
No data transformation was performed for PA variables measured in the two populations.
Data transformation was performed by applying a normalization technique to make all the data values fall within a small range (0 1) to enable the efficient performance of the predictive model (classifier).
Nevertheless, there are no consensus or clear guidelines on the best way to perform the necessary data transformations.
The seemingly highly computationally expensive data transformation is not performed but instead, ingeniously, the separation is performed "implicitly" based on their distances measured with the use of a kernel function.
