A variety of functions exists in R for visualizing and customizing dendrogram. The aim of this article is to describe 5+ methods for drawing a beautiful dendrogram using R software.
To study the relatedness of bacterial strains, banding patterns generated by ERIC and REP-PCR was scored using binary scoring system that recorded the presence and absence of bands as 1 and 0, respectively. To produce dendrogram, a binary matrix was analyzed using dice similarity coefficient and unweighted pair group method for arithmetic averages (UPGMA) [7, 13]. These analysis were carried out using Free Tree software. UPGMA dendrogram was drawn using Treeview [14,15,16].
Upgma Dendrogram Software Free 17
The presence or absence of protein, RAPD, and ISSR bands was scored as 1 for presence or 0 for absence of markers respectively for estimating genetic variation. Euclidian distance Romesburg [32] was calculated and used for measuring the similarity between the 14 samples using the software program, Community Analysis Package 4.0 (CAP) developed and was used according to Seaby and Henderson [33]. The dendrogram was constructed based on the similarity matrix data using the unweighted pair-group method with arithmetic averages (UPGMA) clustering and Free Tree software [34].
For the purpose of comparison with Bayesian-based clustering algorithms, the clustering method based on distance matrix was applied. GDA program [32] was used to obtain matrices of pairwise estimates of genetic distance among 20 populations (according to Nei, 1978, [33]). Based on the obtained matrices and using Neighbour-joining (NJ) and UPGMA algorithms, the program constructed dendrograms, which were subsequently visualized using TreeViewX free software.
The quantities of data obtained by the new high-throughput technologies, such as microarrays or ChIP-Chip arrays, and the large-scale OMICS-approaches, such as genomics, proteomics and transcriptomics, are becoming vast. Sequencing technologies become cheaper and easier to use and, thus, large-scale evolutionary studies towards the origins of life for all species and their evolution becomes more and more challenging. Databases holding information about how data are related and how they are hierarchically organized expand rapidly. Clustering analysis is becoming more and more difficult to be applied on very large amounts of data since the results of these algorithms cannot be efficiently visualized. Most of the available visualization tools that are able to represent such hierarchies, project data in 2D and are lacking often the necessary user friendliness and interactivity. For example, the current phylogenetic tree visualization tools are not able to display easy to understand large scale trees with more than a few thousand nodes. In this study, we review tools that are currently available for the visualization of biological trees and analysis, mainly developed during the last decade. We describe the uniform and standard computer readable formats to represent tree hierarchies and we comment on the functionality and the limitations of these tools. We also discuss on how these tools can be developed further and should become integrated with various data sources. Here we focus on freely available software that offers to the users various tree-representation methodologies for biological data analysis.
Geneious is an integrated, cross-platform bioinformatics research software suite that combines major analysis tools. Geneious is a commercial product and a free trial is available. Features include sequence alignment and phylogenetic analysis. Geneious combines a number of visualization tools for different types of data and analyses. In specific, there is an interactive phylogenetic tree viewer and builder. In addition, there are available plugins for PAUP* and MrBayes.
HCE [64, 65] stands for Hierarchical Clustering Explorer for Interactive Exploration of Multidimensional Data, such as microarray experiment data sets. HCE applies clustering without a predetermined number of groups, and then enables users to determine themselves the acceptable limits via interactive visual feedback, like dendrograms and colorful mosaics. In summary, with HCE one can display hierarchical clustering results and dendrograms or color mosaic displays for multidimensional data sets. An interactive visualization allows users to control the distribution and ranking over one or both dimensions altering thus the clusters. Statistical feedback helps the user to conclude. For the tree visualization part there is a minimum similarity criterion that the user can change and correspondingly view the new formed clusters. Different coloring of the subtrees makes cluster visualization easy. HCE is free for academic and/or research purposes.
TM4 [66] is an open-source, free software. The TM4 suite of tools consists of the following four major applications: Microarray Data Manager (MADAM), TIGR_Spotfinder, Microarray Data Analysis System (MIDAS), Multiexperiment Viewer (MeV). There is also a Minimal Information about a Microarray Experiment (MIAME) compliant MySQL database. All applications are freely available to the scientific research community. TM4 incorporates algorithms for clustering, visualization, classification, statistical analysis and biological theme discovery. TM4 has its own file format, the mev file format. There is a converter that transforms into the mev format data from Genepix, ImaGene, ScanArray, ArrayVersion and Agilent files. Affymetrix data files can be loaded directly into TIGR MeV.
R -project.org/ is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. There are R interfaces for all major programming languages, such as MATLAB, Perl, Python, Java, C, C++ and Fortran. The R package system itself provides implementations for a broad range of statistical and graphical techniques, including modeling and cluster analysis. R is popular in the bioinformatics world as it is free and open source (in contrast to for example, MATLAB). It is highly recommended for biological analysis since lots of documentation is available online.
The dendrogram helps visualize the relationships among clusters, which is the way we use hierarchical data to understand phylogenetics, taxonomy, information architecture, etc. Being resizable (changeable size) and rescalable (adjustable scale in length and width) are desirable for these purposes. However, this graphical representation is static with a fixed size and scale between length and width. A static dendrogram is not resizable and rescalable so that a desired tree view of clusterings is not available. As far as the topic is concerned, almost all existing mathematical or statistical software may not be capable of drawing dendrograms in a free style manner. A nonresizable and nonscalable dendrogram cannot actually meet the needs of viewing clustering results in an arbitrary way. As a result, the static dendrogram is not pragmatically usable but a merely graphical output of data clusterings. Most commercial software can provide only such a simple output function such as MatLab, SPSS and SAS. The typical dendrogram drawing programs available are TreeView, Phylip, Paup, MEGA (Molecular Evolutionary Genetic Analysis) and so on, which provide a representation of graphical clusterings [3] [4] [6] [7]. Some of these programs may be dynamic and the others are static. Some of the dynamic programs (e.g., TreeView) are rescalable but not resizable or vice versa, as well as not in a free-style fashion. Even if these programs are both resizable and rescalable in time, their algorithms are not known to the world or known in different approaches and programming languages (e.g., R and Python). They only possess the basic function for a dendrogram. This impedes the communications between developers to better understand, improve or enhance these algorithms.
With the hierarchical data ClustView.txt, the algorithm is implemented as the most-used hierarchical clustering through the options of being normalized and average linkage (UPGMA). It outputs the dynamically clustering results by drawing each of successive dendrograms as pixels are rearranged. For such a hierarchical cluster analysis, the default forward views (free-style with the smaller, medium and larger sizes and scales as well as colored labels, etc.) are produced respectively. They are exhibited in Figures 1-3 for demonstration of a resizable, rescalable and free-style data clustering visualization. With the phylogenetic data TreeView.tre, the algorithm is implemented as the typically-used phylogenetic or phylogenomic tree drawing. For such a phylogenetic or evolutionary analysis, the default view (forward), mirror view (backward), and upright view (upward with upright and italic labels) are produced respectively. They are exhibited in Figures 4-7 for demonstration of a resizable, rescalable and free-style tree drawing visualization (Figure 8, Figure 9).
With additional algorithm built in the software ParCluster v.3.0, it is possible to save and store the dynamic representation of hierarchical clustering results wherever appropriate. The saved work can be a picture in any of the common formats such as jpeg, tiff and png. This way has a great advantage over other peer applications for a picture saved that has the highest view or publication quality, provided that the right size, scale and style are well adjusted for a demand. This is just the work to do by the algorithm discussed, which is able to make a perfect adjustment for whatever an image quality required by resizing, rescaling and restyling a dendrogram. The information about image size and scale (width x height) can be displayed according to adjustment at the upper-left corner of the window. There is no need for such a quality picture saved to be digitally enlarged or shrunken for a required quality, say, for publication, since it has the preset view quality of the right size, scale and style. 2ff7e9595c
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