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When the SSR data are visualized as a network, potential genetic flow is also supported.
The different sampling levels (populations versus individual accessions) is also the main reason why ITS and trnLF data are visualized as trees and/or networks (phylogenetics), and microsatellite data were subject to population based algorithms.
When these data are visualized as a network, using the BioLayout tool [ 34], most of the nodes (i.e. proteins) are connected (i.e. interacting) in a super-cluster, due to the presence of promiscuous domains.
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Projects data were visualized as feature layers according to the county, project type, and fiscal year.
In this study the data was visualized as two-dimensional graphs using the Gephi [85] software utilizing force-directed algorithms [86, 87].
For selected specimens (see below), mean measured length and width data were visualized as a dendrogram by calculating euclidean distances and applying average-linkage clustering [53] using the statistical software R (http://www.r-project.org/).org/
The resulting data were visualized as Venn diagrams.
The data were visualized as forest plots and receiver operating characteristic curves (ROC).
The data were visualized as dot blots using CellQuest software (BD Biosciences).
Data were visualized as biparametric histograms of log DAPI vs. log side scatter which were triggered on side scatter.
Following normalization, clustering and seriation, data were visualized as heat-maps using the PermutMatrix software (http://www.lirmm.fr/∼caraux/Permut Matrix/) (Caraux & Pinloche, 2005).
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