This vignette demonstrates how to use the
MultifeatureGrid
class from the
MultiModalGraphics
package to create comprehensive heatmaps
integrating various data features such as z-scores, p-values, and
counts.
First, we create a sample dataset representing biological data across different tissues and signaling pathways, with associated p-values and activation z-scores.
data <- get_multifeature_grid_df()
We initialize the MultifeatureGrid
object with the data
prepared above.
mg <- MultifeatureGrid(data)
We then plot the heatmap, specifying ‘tissue’ as the independent variable for faceting.
plot_heatmap(mg, independantVariable = "tissue")
#> Warning: Removed 32 rows containing missing values or values outside the scale range
#> (`geom_point()`).
This plot provides a visual summary of the signaling activity and the statistical significance across different tissues, utilizing a color gradient to represent activation z-scores and the size of points to indicate the number of genes involved.
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