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 provi...
This paper presents a case study to show the competence of our evolutionary and visual framework for...
BackgroundSince the initial publication of clusterMaker, the need for tools to analyze large biologi...
Abstract Background In the post-genomic era, the rapid increase in high-throughput data calls for co...
Background: Studies that aim at explaining phenotypes or disease susceptibility by genetic or epigen...
Background Clustering is one of the most common techniques in data analysis and seeks to group toget...
Abstract Background Studies that aim at explaining phenotypes or disease susceptibility by genetic o...
textabstractBACKGROUND: Accurate interpretation of data obtained by unsupervised analysis of large s...
Abstract Background Accurate interpretation of data obtained by unsupervised analysis of large scale...
Background: The most common method of identifying groups of functionally related genes in microarray...
BACKGROUND: The most common method of identifying groups of functionally related genes in microarray...
Abstract Background In the post-genomic era, the rapi...
AbstractClustering algorithms have been shown to be useful to explore large-scale gene expression pr...
Hierarchical clustering is widely used to find patterns in multi-dimensional datasets, especially fo...
Nowadays, cluster analysis of biological networks has become one of the most important approaches to...
The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of hig...
This paper presents a case study to show the competence of our evolutionary and visual framework for...
BackgroundSince the initial publication of clusterMaker, the need for tools to analyze large biologi...
Abstract Background In the post-genomic era, the rapid increase in high-throughput data calls for co...
Background: Studies that aim at explaining phenotypes or disease susceptibility by genetic or epigen...
Background Clustering is one of the most common techniques in data analysis and seeks to group toget...
Abstract Background Studies that aim at explaining phenotypes or disease susceptibility by genetic o...
textabstractBACKGROUND: Accurate interpretation of data obtained by unsupervised analysis of large s...
Abstract Background Accurate interpretation of data obtained by unsupervised analysis of large scale...
Background: The most common method of identifying groups of functionally related genes in microarray...
BACKGROUND: The most common method of identifying groups of functionally related genes in microarray...
Abstract Background In the post-genomic era, the rapi...
AbstractClustering algorithms have been shown to be useful to explore large-scale gene expression pr...
Hierarchical clustering is widely used to find patterns in multi-dimensional datasets, especially fo...
Nowadays, cluster analysis of biological networks has become one of the most important approaches to...
The Principal Component Analysis (PCA) is a widely used method of reducing the dimensionality of hig...
This paper presents a case study to show the competence of our evolutionary and visual framework for...
BackgroundSince the initial publication of clusterMaker, the need for tools to analyze large biologi...
Abstract Background In the post-genomic era, the rapid increase in high-throughput data calls for co...