cr_enriched_heatmap: Visualize landscape of genome-wide correlations

In jokergoo/epik: Integrative Analysis and Visualization of Epigenomic Sequencing Data

Description

Visualize landscape of genome-wide correlations

Usage

cr_enriched_heatmap(cr, txdb, expr, expr_ha, p_na = 0.25)

Arguments

cr	correlated regions
txdb	transcriptome annotation which was used in correlated_regions
expr	expression matrix which was used in correlated_regions
expr_ha	a HeatmapAnnotation object for the expression heatmap

Details

The landscape of genome-wide correlations is visualized by a list of heatmaps. Each row corresponds to a single gene:

• an enriched heatmap in which correlation signals are normalized at gene bodies

• a point plot showing the length of genes

• a heatmap of gene expression

• a heatmap showing the mean methylation in the extended gene regions.

• a heatmap showing the methylation difference in the extended gene regions.

K-means clustering with four groups is applied on the correlation matrix which has been normalized. The four row subclusters are ordered by mean correlation. So basically, the four groups correspond to negative gene body correlation, weak negative gene body correlation, weak positive gene body correlation and positive gene body correlation. For each subcluster, rows are clustered by the mean methylation matrix.

There is another plot which shows quantitative statistics in the four groups:

• mean gene body correlation

• gene length

• expression difference

• methylation difference

Value

An updated cr that includes the partitioning, this information is important for many downstream analysis. Users should update it by cr = cr_enriched_heatmap(cr, ...).

Author(s)

Zuguang Gu <[e-mail]>

Examples

# There is no example
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jokergoo/epik documentation built on Sept. 28, 2019, 9:20 a.m.