trans_venn: Create 'trans_venn' object for the Venn diagram, petal plot...
In microeco: Microbial Community Ecology Data Analysis
trans_venn R Documentation
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
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Method new()
Usage
trans_venn$new(dataset, ratio = NULL, name_joint = "&")
Arguments
datasetthe object of microtable class or a matrix-like table (data.frame or matrix object).
If dataset is a matrix-like table, features must be rows.
ratiodefault NULL; NULL, "numratio" or "seqratio"; "numratio": calculate the percentage of feature number;
"seqratio": calculate the percentage of feature abundance; NULL: no additional percentage.
name_jointdefault "&"; the joint mark for generating multi-sample names.
Returns
data_details and data_summary stored in the object.
Examples
\donttest{
data(dataset)
t1 <- dataset$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_circledefault RColorBrewer::brewer.pal(8, "Dark2"); color pallete.
fill_colordefault TRUE; whether fill the area color.
text_sizedefault 4.5; text size in plot.
text_name_sizedefault 6; name size in plot.
text_name_positiondefault NULL; name position in plot.
alphadefault .3; alpha for transparency.
linesizedefault 1.1; cycle line size.
petal_plotdefault FALSE; whether use petal plot.
petal_colordefault "#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_centerdefault "#BEBADA"; color of the center in the petal plot.
petal_adefault 4; the length of the ellipse.
petal_rdefault 1; scaling up the size of the ellipse.
petal_use_limdefault c(-12, 12); the width of the plot.
petal_center_sizedefault 40; petal center circle size.
petal_move_xydefault 4; the distance of text to circle.
petal_move_kdefault 2.3; the distance of title to circle.
petal_move_k_countdefault 1.3; the distance of data text to circle.
petal_text_movedefault 40; the distance between two data text.
other_text_showdefault NULL; other characters used to show in the plot.
other_text_positiondefault c(1, 1); the text position for text in other_text_show.
other_text_sizedefault 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 = TRUE,
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_plotdefault TRUE; whether add the left bar plot to show the feature number of each sample.
sort_samplesdefault TRUE; whether sort samples according to the number of features in each sample.
If FALSE, use the sample orders in sample_table of the raw dataset.
up_y_titledefault "Intersection set"; y axis title of upper plot.
up_y_title_sizedefault 15; y axis title size of upper plot.
up_y_text_sizedefault 4; y axis text size of upper plot.
up_bar_filldefault "grey70"; bar fill color of upper plot.
bottom_y_text_sizedefault 12; y axis text size, i.e. sample name size, of bottom sample plot.
bottom_heightdefault 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_sizedefault 3; point size of bottom plot.
bottom_point_colordefault "black"; point color of bottom plot.
bottom_background_filldefault "grey95"; fill color for the striped background in the bottom sample plot.
left_widthdefault 0.3; left bar plot width relative to the right bottom plot.
left_bar_filldefault "grey70"; fill color for the left bar plot presenting feature number.
left_x_text_sizedefault 10; x axis text size of the left bar plot.
left_background_filldefault "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_frequencydefault 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$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `trans_venn$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)
microeco documentation built on Nov. 18, 2023, 9:06 a.m.
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| trans_venn | R Documentation |
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
Method new()
Usage
trans_venn$new(dataset, ratio = NULL, name_joint = "&")
Arguments
datasetthe object of
microtableclass or a matrix-like table (data.frame or matrix object). If dataset is a matrix-like table, features must be rows.ratiodefault NULL; NULL, "numratio" or "seqratio"; "numratio": calculate the percentage of feature number; "seqratio": calculate the percentage of feature abundance; NULL: no additional percentage.
name_jointdefault "&"; the joint mark for generating multi-sample names.
Returns
data_details and data_summary stored in the object.
Examples
\donttest{
data(dataset)
t1 <- dataset$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_circledefault
RColorBrewer::brewer.pal(8, "Dark2"); color pallete.fill_colordefault TRUE; whether fill the area color.
text_sizedefault 4.5; text size in plot.
text_name_sizedefault 6; name size in plot.
text_name_positiondefault NULL; name position in plot.
alphadefault .3; alpha for transparency.
linesizedefault 1.1; cycle line size.
petal_plotdefault FALSE; whether use petal plot.
petal_colordefault "#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_centerdefault "#BEBADA"; color of the center in the petal plot.
petal_adefault 4; the length of the ellipse.
petal_rdefault 1; scaling up the size of the ellipse.
petal_use_limdefault c(-12, 12); the width of the plot.
petal_center_sizedefault 40; petal center circle size.
petal_move_xydefault 4; the distance of text to circle.
petal_move_kdefault 2.3; the distance of title to circle.
petal_move_k_countdefault 1.3; the distance of data text to circle.
petal_text_movedefault 40; the distance between two data text.
other_text_showdefault NULL; other characters used to show in the plot.
other_text_positiondefault c(1, 1); the text position for text in
other_text_show.other_text_sizedefault 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 = TRUE, 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_plotdefault TRUE; whether add the left bar plot to show the feature number of each sample.
sort_samplesdefault TRUE; whether sort samples according to the number of features in each sample. If FALSE, use the sample orders in sample_table of the raw dataset.
up_y_titledefault "Intersection set"; y axis title of upper plot.
up_y_title_sizedefault 15; y axis title size of upper plot.
up_y_text_sizedefault 4; y axis text size of upper plot.
up_bar_filldefault "grey70"; bar fill color of upper plot.
bottom_y_text_sizedefault 12; y axis text size, i.e. sample name size, of bottom sample plot.
bottom_heightdefault 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_sizedefault 3; point size of bottom plot.
bottom_point_colordefault "black"; point color of bottom plot.
bottom_background_filldefault "grey95"; fill color for the striped background in the bottom sample plot.
left_widthdefault 0.3; left bar plot width relative to the right bottom plot.
left_bar_filldefault "grey70"; fill color for the left bar plot presenting feature number.
left_x_text_sizedefault 10; x axis text size of the left bar plot.
left_background_filldefault "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_frequencydefault 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$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `trans_venn$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)
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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