DATASET_SIMPLEBAR.Rmd
The DATASET_SIMPLEBAR
template is to draw the simple bar
charts and store corresponding numeric value. The
DATASET_SIMPLEBAR
template belongs to the “Basic graphics”
class (refer to the Class for detail information).
In simple bar chart, each tree node is associated to a single numeric value, which is displayed as a bar outside the tree. This value is 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 pictures or sharing them with others.
Here, itol.toolkit
provide a convenient way to calculate
statistics of simple bar charts. This section describes
how to use itol.toolkit
to prepare the simple bar chart
templates.
This section uses dataset 1 as an example to show how to draw the Simple bar charts. (refer to the Dataset for detail information)
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 <- 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),",","-")
Let’s say the user intends to calculate the sum of each template’s frequency in 21 articles as a basis for creating Simple bar charts. In the event that the user enters multiple columns of data without specifying a method, itol.toolkit will automatically use the sum method to process the data.
unit_29 <- create_unit(data = df_frequence,
key = "E029_simplebar_1",
type = "DATASET_SIMPLEBAR",
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 this as a basis for drawing Simple bar
charts.
unit_30 <- create_unit(data = df_frequence,
key = "E030_simplebar_2",
type = "DATASET_SIMPLEBAR",
method = "mean",
tree = tree)
By adjusting unit@specific_themes$basic_plot$size_max
,
users can customize the size of the plot. Here, we set a standard size
for E029 and E030.
unit@specific_themes$basic_plot$size_max <- 100
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