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Conceptually, each data item can be viewed as a point in a multidimensional space with the dimensions representing the physical features we record.
PCoA was performed in a multidimensional space with data standardization using GENALEX v.6.41 [ 67].
Gene profiles can be viewed as points in multidimensional space with the dimensionality given by the number of measurements.
It enables graphical visualization of individuals in multidimensional space, with no a priori assumptions about grouping, using each allele as an independent variable.
This method allows visualizing population (group) barycentres in a multidimensional space with as many dimensions as total number of alleles summed over all loci.
After sequencing reads are processed, each subject is represented by a p-dimensional vector (p = 597) in the multidimensional space, with each coordinate corresponding to the relative abundance of each microbial species.
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The hypercube function (HC) is a hypothetical function connected with multidimensional space.
The exemplary non-transitory computer-readable medium, method and system can retrieve information from data associated with the multidimensional space.
In a first step, we demonstrate that our vectorial representation of RNA structures and the Euclidian distance in the multidimensional space consequently defined is comparable with the sequence/structure similarities identified by LocARNA a conventional structural clustering method.
The standardized data were further analyzed using unconstrained Principal Components Analysis to plot samples in multidimensional space using the R statistical package with the 'vegan' library.
We then used principal components 1 and 2 (PC2, PC2) to extract the locations in multidimensional space of the reference barriers associated with our reference ring taxa.
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