DATASET_GRADIENT

Weiyue Liu1, Zhongyi Hua2, Tong Zhou3

Last compiled on 07 December, 2023

Introduction

The function of DATASET_GRADIENT is to associate specified tree nodes to numeric values, which is displayed as a colored rectangle outside the tree. The DATASET_GRADIENT template belongs to the “Basic graphics” class (refer to the Class for detail information).

Typically, users would have to enter the leaf node name and the corresponding value in pairs to generate a colored rectangle outside the corresponding tip node. However, the raw data users need to process are usually to be the multi-column wide data (e.g. data from multiple biological replicates), which need users to calculate a statistic (e.g. average value). Thus, the data analysis and iTOL visualization preparation are separated without itol.toolkit.

This section shows how to use itol.toolkit to add corresponding numerical values to tip nodes. Users can directly enter multi-column data into itol.toolkit. The program will automatically calculate statistics from input data using a batch of methods, making the workflow of data analysis and visualization coherent. In general, Users can determine data processing methods based on their needs and output templates directly.

Visualize numerical information

This section uses dataset 1 as an example to show the visualization of binary data in different types of trees (refer to the Dataset.

The first step is to load the newick format tree file tree_of_itol_templates.tree and its corresponding metadata template_frequence. Briefly, the templates_frequence contains the usage of each template type in 21 published studies.

library(itol.toolkit)
library(data.table)
library(ape)
tree <- system.file("extdata",
                    "tree_of_itol_templates.tree",
                    package = "itol.toolkit")
df_frequence <- system.file("extdata",
                            "templates_frequence.txt",
                             package = "itol.toolkit")
df_frequence <- fread(df_frequence)

Suppose users want to sum the frequency of each template used in 21 articles, and visualizing it using gradient color, users could use following codes. Notably, if users enter multiple columns of data and does not specify a method, itol.toolkit uses the sum method to process the data by default.

unit_23 <- create_unit(data = df_frequence,
                       key = "E023_gradient_1",
                       type = "DATASET_GRADIENT",
                       tree = tree)

We can also calculate the average usage frequency of each template in published articles by specifying the method parameter as mean, and use it in gradient coloring.

unit_24 <- create_unit(data = df_frequence,
                       key = "E024_gradient_2",
                       type = "DATASET_GRADIENT",
                       method = "mean",
                       tree = tree)

Style modification

By adjusting unit@specific_themes$heatmap$color$min, unit@specific_themes$heatmap$color$mid, and unit@specific_themes$heatmap$color$max, users can customize the color gradient of the rectangle. Here, we set different colors for unit_23 and unit_24.

unit_23@specific_themes$heatmap$color$min <- "#0000ff"
unit_23@specific_themes$heatmap$color$max <- "#ff0000"

unit_24@specific_themes$heatmap$color$min <- "#FFF7ED"
unit_24@specific_themes$heatmap$color$mid <- "#FC8E58"
unit_24@specific_themes$heatmap$color$max <- "#800000"

  1. IOCAS, ↩︎

  2. CACMS, ↩︎

  3. IOCAS, ↩︎