genomics and proteomics data are typically analyzed by hierarchical clustering , followed by visualization with heatmaps [ 1 - 3 ] . the visualization of clustered data includes tree - based hierarchical clustering patterns and heatmaps of experimental values . simultaneously carrying out clustering and visualization in a single platform provides a convenient tool for choosing an appropriate clustering algorithm and finding patterns in the resulting heatmaps . previously , bioinformaticists used programmable tools , such as r and matlab , and commercial data - mining packages to analyze their data . a simple and integrated program will allow experimental scientists to intuitively identify meaningful patterns from a large dataset without requiring knowledge of scripting computer languages or statistical theory . herein , we introduce a user - friendly tool , qcanvas , which integrates diverse clustering algorithms and an interactive heatmap display interface ( fig . this program directly imports raw experimental data in a matrix format and displays these data in a heatmap . various clustering methods can be applied to two - dimensional data , with the real - time generation of clustered heatmaps . furthermore , subsets of heatmap data can be selectively displayed , based on user - defined filters . qcanvas is an easy - to - use and powerful tool for fast data analysis and interpretation by bench scientists . without any knowledge of scripting languages and without any graphics - editing software , one can generate and customize tree - clustered heatmaps with high - quality graphics qcanvas provides a total of eight popular measures for generating the similarity matrix - i.e . , correlation uncenter , correlation center , absolute corr - uncenter , absolute corrcenter , spearman rank , kendall 's tau , euclidean distance , and city - block distance . all of these measures have typically been included among the data clustering methods of previous tools . in qcanvas , the calculation of the similarity matrix is selectively applied to the data for the x - axis and the y - axis independently . qcanvas provides diverse algorithms for hierarchical clustering , such as the average method , centroid method , single method , and complete method . qcanvas uses a standard window - based graphical user interface ( gui ) , providing multiple windows to comparatively visualize patterns of various combinations of similarity matrices and hierarchical clustering methods . qcanvas software recognizes text - based data in a matrix format . for demonstration purposes , a small microarray gene expression dataset is included in the software package and can be downloaded from the website ( http://compbio.sookmyung.ac.kr/~qcanvas ) . once the input data are imported into the qcanvas window , a heatmap of the non - clustered data is displayed ( fig . the user can easily test various data - clustering and tree - building methods on the raw data and interactively select appropriate heatmaps with tree structures ( fig . the gui provides various menu - based options to optimize the display of heatmaps , trees , and annotations . the colors , locations , and sizes of the trees and the annotations can be customized in a flexible manner . the scale and color scheme of the heatmaps can also be adjusted in an interactive window . the node colors can be customized for positive , negative , missing , or zero values . the overall vertical or horizontal size of a component of a figure can be customized and saved in postscript format for a high - image quality . heatmaps that are based on data clustering display the overall profiles of the experimental values for the given samples . qcanvas provides a data - filtering option to selectively display data nodes satisfying a given threshold . in the example shown in fig . 2c , data points with a 2-fold change ( increase or decrease ) in gene expression are selectively displayed . in many cases , the option for data filtering in qcanvas is useful for analyzing patterns in the experimental data that are statistically significant . one can filter the heatmap profiles using statistical confidence data that are included in a separate file . in the example shown in fig . 2d , the gene expression data are filtered based on the p - values for the fold - change . the gui menu for data filtering enables the pattern analysis to be performed easily , without the need for manual data processing or the use of scripting languages . qcanvas provides a total of eight popular measures for generating the similarity matrix - i.e . , correlation uncenter , correlation center , absolute corr - uncenter , absolute corrcenter , spearman rank , kendall 's tau , euclidean distance , and city - block distance . all of these measures have typically been included among the data clustering methods of previous tools . in qcanvas , the calculation of the similarity matrix is selectively applied to the data for the x - axis and the y - axis independently . qcanvas provides diverse algorithms for hierarchical clustering , such as the average method , centroid method , single method , and complete method . qcanvas uses a standard window - based graphical user interface ( gui ) , providing multiple windows to comparatively visualize patterns of various combinations of similarity matrices and hierarchical clustering methods . qcanvas software recognizes text - based data in a matrix format . for demonstration purposes , a small microarray gene expression dataset is included in the software package and can be downloaded from the website ( http://compbio.sookmyung.ac.kr/~qcanvas ) . once the input data are imported into the qcanvas window , a heatmap of the non - clustered data is displayed ( fig . the user can easily test various data - clustering and tree - building methods on the raw data and interactively select appropriate heatmaps with tree structures ( fig . the gui provides various menu - based options to optimize the display of heatmaps , trees , and annotations . the colors , locations , and sizes of the trees and the annotations can be customized in a flexible manner . the scale and color scheme of the heatmaps can also be adjusted in an interactive window . the node colors can be customized for positive , negative , missing , or zero values . the overall vertical or horizontal size of a component of a figure can be customized and saved in postscript format for a high - image quality . heatmaps that are based on data clustering display the overall profiles of the experimental values for the given samples . qcanvas provides a data - filtering option to selectively display data nodes satisfying a given threshold . in the example shown in fig . 2c , data points with a 2-fold change ( increase or decrease ) in gene expression are selectively displayed . in many cases , the option for data filtering in qcanvas is useful for analyzing patterns in the experimental data that are statistically significant . one can filter the heatmap profiles using statistical confidence data that are included in a separate file . in the example shown in fig . 2d , the gene expression data are filtered based on the p - values for the fold - change . qcanvas can import two separate files together for simultaneous data clustering and filtering . the gui menu for data filtering enables the pattern analysis to be performed easily , without the need for manual data processing or the use of scripting languages . this report introduces qcanvas , a program that provides a convenient and powerful interface for the pattern analysis of large - scale omics data . this program enables the user to conduct data clustering , data filtering , and graphics editing simultaneously on an integrated platform . these steps are typically performed on omics data , such as dna ( or protein ) microarray data . the simple and intuitive nature of this tool meets the practical needs of research scientists working on omics data who do not have expertise in bioinformatics approaches . the program is freely available with demo data and a step - by - step tutorial through the website ( http://compbio.sookmyung.ac.kr/~qcanvas ) .
we developed a user - friendly , interactive program to simultaneously cluster and visualize omics data , such as dna and protein array profiles . 
 this program provides diverse algorithms for the hierarchical clustering of two - dimensional data . 
 the clustering results can be interactively visualized and optimized on a heatmap . 
 the present tool does not require any prior knowledge of scripting languages to carry out the data clustering and visualization . 
 furthermore , the heatmaps allow the selective display of data points satisfying user - defined criteria . 
 for example , a clustered heatmap of experimental values can be differentially visualized based on statistical values , such as p - values . including diverse menu - based display options 
 , qcanvas provides a convenient graphical user interface for pattern analysis and visualization with high - quality graphics .