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Data matrices were transformed using the double square root transformation, converted into triangular matrices of similarities using the Bray Curtis coefficient and were subjected to group-average linking method.
The matrices were transformed to probability matrices that delineate the probability of each nucleotide to appear in each position in the TFBS.
To reconstruct individual networks the correlation coefficient matrices were transformed into binary adjacency matrices according to a<0.01 [99%] p-value threshold corrected for a multiple comparisons using Bonferroni correction.
Briefly, weighted correlation matrices were transformed into matrices of connection strengths using a power function [ 13].
The similarity matrices were transformed to distances for input into the vegan 2.0-7 package (Oksanen et al. 2013).
Feature abundance matrices were transformed by adding 1 to each feature and calculating log10 subsequently, avoiding negative infinite values for absent features.
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It is shown that one may eliminate periodic coefficients from the transformed equations of motion, even when the matrices being transformed do not satisfy the "multiplicity condition".
The matrices are transformed into log-ratio matrices following the technique given in [ 18].
The matrixes were transformed to canonical factors that were linear combinations of the response patterns.
The values present in the expression matrix were transformed into log2 and a design matrix was defined to describe the biological samples.
For the transformation step, the gene expression matrix was transformed into pathway-based, GO-based, and Pfam-based matrices, that we call knowledge- based datasets.
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matrices were incorporated
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matrices were altered
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matrices were composed
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