Abstract
High-throughput sequencing data are dramatically increasing in volume. Thus, there is urgent need for efficient tools to perform fast and integrative analysis of multiple data types. Enriched heatmap is a specific form of heatmap that visualizes how genomic signals are enriched over specific target regions. It is commonly used and efficient at revealing enrichment patterns especially for high dimensional genomic and epigenomic datasets. We present a new R package named EnrichedHeatmap that efficiently visualizes genomic signal enrichment. It provides advanced solutions for normalizing genomic signals within target regions as well as offering highly customizable visualizations. The major advantage of EnrichedHeatmap is the ability to conveniently generate parallel heatmaps as well as complex annotations, which makes it easy to integrate and visualize comprehensive overviews of the patterns and associations within and between complex datasets. EnrichedHeatmap facilitates comprehensive understanding of high dimensional genomic and epigenomic data. The power of EnrichedHeatmap is demonstrated by visualization of the complex associations between DNA methylation, gene expression and various histone modifications.
Highlights
High-throughput sequencing data are dramatically increasing in volume
27 samples are separated into two subgroups that correspond to embryonic cells and mature cells
Rows are clustered based on the closeness of regions showing significant negative correlation between methylation and gene expression
Summary
High-throughput sequencing data are dramatically increasing in volume. there is urgent need for efficient tools to perform fast and integrative analysis of multiple data types. Enriched heatmap is a specific form of heatmap that visualizes how genomic signals are enriched over specific target regions. It is commonly used and efficient at revealing enrichment patterns especially for high dimensional genomic and epigenomic datasets. With increasing accessibility and application of high throughput sequencing methods, there is a rise in the number of complex genomic and epigenomics studies. A common task for integrative visualization is to study how various genomic signals are enriched over specific genomic targets. Genomation [4] is an R package which visualizes enrichment for multiple types of signals simultaneously, but the functionality is very limited and difficult for more complex visualizations
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