trans_venn {microeco}

Create trans_venn object for the Venn diagram, petal plot and UpSet plot.

Description

This class is a wrapper for a series of intersection analysis related methods, including 2- to 5-way venn diagram, more than 5-way petal or UpSet plot and intersection transformations based on David et al. (2012) <doi:10.1128/AEM.01459-12>.

Methods

Public methods

• trans_venn$new()

• trans_venn$plot_venn()

• trans_venn$plot_bar()

• trans_venn$trans_comm()

• trans_venn$print()

• trans_venn$clone()

Method new()

Usage

trans_venn$new(dataset, ratio = NULL, name_joint = "&")

Arguments

dataset

the object of microtable class or a matrix-like table (data.frame or matrix object). If dataset is a matrix-like table, features must be rows.

ratio

default NULL; NULL, "numratio" or "seqratio"; "numratio": calculate the percentage of feature number; "seqratio": calculate the percentage of feature abundance; NULL: no additional percentage.

name_joint

default "&"; the joint mark for generating multi-sample names.

Returns

data_details and data_summary stored in the object.

Examples

\donttest{
data(dataset)
t1 <- dataset$merge_samples(use_group = "Group")
t1 <- trans_venn$new(dataset = t1, ratio = "numratio")
}

Method plot_venn()

Plot venn diagram.

Usage

trans_venn$plot_venn(
  color_circle = RColorBrewer::brewer.pal(8, "Dark2"),
  fill_color = TRUE,
  text_size = 4.5,
  text_name_size = 6,
  text_name_position = NULL,
  alpha = 0.3,
  linesize = 1.1,
  petal_plot = FALSE,
  petal_color = "#BEAED4",
  petal_color_center = "#BEBADA",
  petal_a = 4,
  petal_r = 1,
  petal_use_lim = c(-12, 12),
  petal_center_size = 40,
  petal_move_xy = 4,
  petal_move_k = 2.3,
  petal_move_k_count = 1.3,
  petal_text_move = 40,
  other_text_show = NULL,
  other_text_position = c(2, 2),
  other_text_size = 5
)

Arguments

color_circle

default RColorBrewer::brewer.pal(8, "Dark2"); color pallete.

fill_color

default TRUE; whether fill the area color.

text_size

default 4.5; text size in plot.

text_name_size

default 6; name size in plot.

text_name_position

default NULL; name position in plot.

alpha

default .3; alpha for transparency.

linesize

default 1.1; cycle line size.

petal_plot

default FALSE; whether use petal plot.

petal_color

default "#BEAED4"; color of the petals; If petal_color only has one color value, all the petals will be assigned with this color value. If petal_color has multiple colors, and the number of color values is smaller than the petal number, the function can append more colors automatically with the color interpolation.

petal_color_center

default "#BEBADA"; color of the center in the petal plot.

petal_a

default 4; the length of the ellipse.

petal_r

default 1; scaling up the size of the ellipse.

petal_use_lim

default c(-12, 12); the width of the plot.

petal_center_size

default 40; petal center circle size.

petal_move_xy

default 4; the distance of text to circle.

petal_move_k

default 2.3; the distance of title to circle.

petal_move_k_count

default 1.3; the distance of data text to circle.

petal_text_move

default 40; the distance between two data text.

other_text_show

default NULL; other characters used to show in the plot.

other_text_position

default c(1, 1); the text position for text in other_text_show.

other_text_size

default 5; the text size for text in other_text_show.

Returns

ggplot.

Examples

\donttest{
t1$plot_venn()
}

Method plot_bar()

Plot the intersections using histogram, i.e. UpSet plot. Especially useful when samples > 5.

Usage

trans_venn$plot_bar(
  left_plot = TRUE,
  sort_samples = FALSE,
  up_y_title = "Intersection size",
  up_y_title_size = 15,
  up_y_text_size = 8,
  up_bar_fill = "grey70",
  bottom_y_text_size = 12,
  bottom_height = 1,
  bottom_point_size = 3,
  bottom_point_color = "black",
  bottom_background_fill = "grey95",
  left_width = 0.3,
  left_bar_fill = "grey70",
  left_x_text_size = 10,
  left_background_fill = "grey95"
)

Arguments

left_plot

default TRUE; whether add the left bar plot to show the feature number of each sample.

sort_samples

default FALSE; TRUE is used to sort samples according to the number of features in each sample. FALSE means the sample order is same with that in sample_table of the raw dataset.

up_y_title

default "Intersection set"; y axis title of upper plot.

up_y_title_size

default 15; y axis title size of upper plot.

up_y_text_size

default 4; y axis text size of upper plot.

up_bar_fill

default "grey70"; bar fill color of upper plot.

bottom_y_text_size

default 12; y axis text size, i.e. sample name size, of bottom sample plot.

bottom_height

default 1; bottom plot height relative to the upper bar plot. 1 represents the height of bottom plot is same with the upper bar plot.

bottom_point_size

default 3; point size of bottom plot.

bottom_point_color

default "black"; point color of bottom plot.

bottom_background_fill

default "grey95"; fill color for the striped background in the bottom sample plot.

left_width

default 0.3; left bar plot width relative to the right bottom plot.

left_bar_fill

default "grey70"; fill color for the left bar plot presenting feature number.

left_x_text_size

default 10; x axis text size of the left bar plot.

left_background_fill

default "grey95"; fill color for the striped background in the left plot.

Returns

a ggplot2 object.

Examples

\donttest{
t2 <- t1$plot_bar()
}

Method trans_comm()

Transform intersection result to community-like microtable object for further composition analysis.

Usage

trans_venn$trans_comm(use_frequency = TRUE)

Arguments

use_frequency

default TRUE; whether only use OTUs occurrence frequency, i.e. presence/absence data; if FALSE, use abundance data.

Returns

a new microtable class.

Examples

\donttest{
t2 <- t1$trans_comm(use_frequency = TRUE)
}

Method print()

Print the trans_venn object.

Usage

trans_venn$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

trans_venn$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

## ------------------------------------------------
## Method `trans_venn$new`
## ------------------------------------------------


data(dataset)
t1 <- dataset$merge_samples(use_group = "Group")
t1 <- trans_venn$new(dataset = t1, ratio = "numratio")


## ------------------------------------------------
## Method `trans_venn$plot_venn`
## ------------------------------------------------


t1$plot_venn()


## ------------------------------------------------
## Method `trans_venn$plot_bar`
## ------------------------------------------------


t2 <- t1$plot_bar()


## ------------------------------------------------
## Method `trans_venn$trans_comm`
## ------------------------------------------------


t2 <- t1$trans_comm(use_frequency = TRUE)