This example demonstrates how to use the ClearScatterplot
class to create enhanced scatterplots. The ClearScatterplot
class provides functionalities for creating scatterplots with enhanced visualization features like coloring based on significance levels and plotting against different variables.
First, load the necessary data and create an instance of ClearScatterplot
.
```{r load-data} plotdata <- get_clear_scatterplot_df()
scatterplotObject <- ClearScatterplot( data = plotdata, logFoldChange = “log2fc”, timePointColumn = “timePoint”, timePointLevels = c(“T10R1”, “T5R1”) )
Next, create the scatterplot using the `createPlot` method, specifying various
parameters such as color and thresholds.
```{r create-plot}
scattered_plot <-
createPlot(
scatterplotObject,
color1 = "cornflowerblue",
color2 = "grey",
color3 = "indianred",
highLog2fc = 0.585,
lowLog2fc = -0.585,
negLog10pValue = 1.301,
expressionDirection = "regulation",
negativeLogPValue = "negLog10p",
timeVariable = "reg_time_org",
xAxis = "organ",
yAxis = "timePoint"
)
Finally, display the plot. This will call the ‘show’ method to render the plot.
{r display-plot} scattered_plot # Display the plot
This section demonstrates how to create and visualize an informative heatmap using the MultiModalGraphics
package which utilizes the ComplexHeatmap
package for enhanced visualizations.
Below is an example of how to create an informative heatmap with data representing genes, their value groups, and significance levels.
First, we load the necessary data.
{r load-data} informative_heatmap <- get_informative_heatmap_df() informative_heatmap_matrix <- as.matrix(informative_heatmap) group_val <- informative_heatmap_matrix[, 1:3] p_val <- informative_heatmap_matrix[, 4:6]
Create an InformativeHeatmap
object with custom settings for visual representation.
{r create-heatmap} htmp_plus <- InformativeHeatmap(group_val, unit_val = 7, pch_val = 16, significant_color = "black", trending_color = "turquoise", significant_pvalue = 0.05, trending_pvalue = 0.1, significance_level = p_val, row_title = "Genes", column_title = "Value and Significance", cluster_rows = TRUE, show_row_names = TRUE, row_names_side = "left", column_names_rot = 45, row_dend_reorder = TRUE, rect_gp = gpar(col = "white", lwd = 2))
This example 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.
```{r example-data} data <- get_multifeature_grid_df()
## Creating a MultifeatureGrid Object
We initialize the `MultifeatureGrid` object with the data prepared above.
```{r create-grid}
mg <- MultifeatureGrid(data)
We then plot the heatmap, specifying ‘tissue’ as the independent variable for faceting.
{r plot-heatmap} plot_heatmap(mg, independantVariable = "tissue")
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.