Heatmap optimization for pattern recognition
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. 2A). 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. 2B). 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 color contrast between nodes can also be interactively adjusted. 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.