DATASET MULTIBAR

Longzhao Li1, Zhongyi Hua2, Tong Zhou3

Last compiled on 07 December, 2023

Introduction

The function of DATASET_MULTIBAR is to prepare templates for drawing multi-value bar charts. In multibar charts, each tip node is associated to multiple numeric values, which are displayed as a stacked or aligned bar chart outside the tree. The DATASET_MULTIBAR template belongs to the “Basic graphics” class (refer to the Class for detail information).

In multibar charts, individual fields (values) require corresponding labels and colors that are defined using “FIELD_LABELS” and “FIELD_COLOR” lines in the template. Additionally, these values are typically statistics derived from raw data, such as the average and sum. Unfortunately, iTOL does not support statistical analysis, making it necessary for users to use other tools to perform such analysis. Additionally, the raw data was excluded in iTOL templates, posing difficulties in reproducing figures or sharing them with others.

Here, we provide a convenient way to calculate statistics for multibar charts and store FIELD_LABELS and FIELD_COLOR values. This section describes how to use itol.toolkit to prepare the multi-value bar charts templates.

Plot multibar plot

This section uses dataset 1 as an example to show how to draw the multibar charts. (refer to the Dataset for detail information)

Load data

The first step is to load the newick format tree file tree_of_itol_templates.tree and its corresponding metadata df_frequence.

library(itol.toolkit)
library(data.table)
library(tidyr)
library(dplyr)
library(stringr)
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 <- data.table::fread(df_frequence)
names(df_frequence) <- c(
  "id",
  "Li,S. et al. (2022) J. Hazard. Mater.","Zheng,L. et al. (2022) Environ. Pollut.",
  "Welter,D.K. et al. (2021) mSystems",
  "Zhang,L et al. (2022) Nat. Commun.",
  "Rubbens,P. et al. (2019) mSystems",
  "Laidoudi,Y. et al. (2022) Pathogens",
  "Wang,Y. et al. (2022) Nat. Commun.",
  "Ceres,K.M. et al. (2022) Microb. Genomics",
  "Youngblut,N.D. et al. (2019) Nat. Commun.",
  "Balvín,O. et al. (2018) Sci. Rep.",
  "Prostak,S.M. et al. (2021) Curr. Biol.",
  "Dijkhuizen,L.W. et al. (2021) Front. Plant Sci.",
  "Zhang,X. et al. (2022) Microbiol. Spectr.",
  "Peris,D. et al. (2022) PLOS Genet.",
  "Denamur,E. et al. (2022) PLOS Genet.",
  "Dezordi,F.Z. et al. (2022) bioRxiv",
  "Lin,Y. et al. (2021) Microbiome",
  "Wang,Y. et al. (2022) bioRxiv",
  "Qi,Z. et al. (2022) Food Control",
  "Zhou,X. et al. (2022) Food Res. Int.",
  "Zhou,X. et al. (2022) Nat. Commun.")
names(df_frequence) <- stringr::str_remove_all(names(df_frequence),"[()]")
names(df_frequence) <- stringr::str_replace_all(names(df_frequence),",","-")

Data processing and create the unit

Convert wide data to long data. After conversion, the input data fed to DATASET_MULTIBAR should have at least two columns: The first column is tip id and the other should contain the values to plot. The itol.toolkit will automatically assigned FIELD_LABELSby columns names and FIELD_COLORS by the palette.

df_frequence_years <- df_frequence %>%
   pivot_longer(-id)%>%
   na.omit() %>%
   mutate(years = str_extract(name,"\\d{4}")) %>%
   group_by(id,years) %>%
   summarise(value = sum(value)) %>%
   spread(years,value) %>%
   replace(is.na(.), 0)
unit_30 <- create_unit(data = df_frequence_years,
                    key = "E030_simplebar_2", 
                    type = "DATASET_MULTIBAR", 
                    tree = tree)
write_unit(unit_30)

  1. IOCAS, ↩︎

  2. CACMS, ↩︎

  3. IOCAS, ↩︎