R: Create 'trans_beta' object for beta-diversity analysis

trans_beta {microeco}

R Documentation

Create `trans_beta` object for beta-diversity analysis

Description

This class is a wrapper for a series of beta-diversity related analysis, including ordination analysis based on An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035>, group distance comparision, clustering, perMANOVA based on Anderson al. (2008) <doi:10.1111/j.1442-9993.2001.01070.pp.x>, ANOSIM and PERMDISP. Note that the beta diversity analysis methods related with environmental variables are encapsulated within the trans_env class.

Methods

Method `new()`

Usage

trans_beta$new(dataset = NULL, measure = NULL, group = NULL)

Arguments

dataset: the object of microtable class.
measure: default NULL; a matrix name stored in microtable$beta_diversity list, such as "bray" or "jaccard", or a customized matrix; used for ordination, manova, group distance comparision, etc.; Please see cal_betadiv function of microtable class for more details.
group: default NULL; sample group used for manova, betadisper or group distance comparision.

Returns

measure, group and dataset stored in the object.

Examples

data(dataset)
t1 <- trans_beta$new(dataset = dataset, measure = "bray", group = "Group")

Method `cal_ordination()`

Unconstrained ordination.

Usage

trans_beta$cal_ordination(
  method = "PCoA",
  ncomp = 3,
  trans = FALSE,
  scale_species = FALSE,
  scale_species_ratio = 0.8,
  ...
)

Arguments

method: default "PCoA"; "PCA", "DCA", "PCoA" or "NMDS". PCA: principal component analysis; DCA: detrended correspondence analysis; PCoA: principal coordinates analysis; NMDS: non-metric multidimensional scaling. Please refer to the paper <doi:10.1111/j.1574-6941.2007.00375.x> for the details of the methods.
ncomp: default 3; dimensions shown in the results (except method "NMDS").
trans: default FALSE; whether species abundance will be square transformed; only available when method is "PCA" or "DCA".
scale_species: default FALSE; whether species loading in PCA or DCA is scaled.
scale_species_ratio: default 0.8; the ratio to scale up the loading; multiply by the maximum distance between samples and origin. Only available when scale_species = TURE.
...: parameters passed to vegan::rda function when method = "PCA", or vegan::decorana function when method = "DCA", or ape::pcoa function when method = "PCoA", or vegan::metaMDS function when when method = "NMDS".

Returns

res_ordination stored in the object.

Examples

t1$cal_ordination(method = "PCoA")

Method `plot_ordination()`

Plot the ordination result.

Usage

trans_beta$plot_ordination(
  plot_type = "point",
  color_values = RColorBrewer::brewer.pal(8, "Dark2"),
  shape_values = c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14),
  plot_color = NULL,
  plot_shape = NULL,
  plot_group_order = NULL,
  add_sample_label = NULL,
  point_size = 3,
  point_alpha = 0.8,
  centroid_segment_alpha = 0.6,
  centroid_segment_size = 1,
  centroid_segment_linetype = 3,
  ellipse_chull_fill = TRUE,
  ellipse_chull_alpha = 0.1,
  ellipse_level = 0.9,
  ellipse_type = "t",
  NMDS_stress_pos = c(1, 1),
  NMDS_stress_text_prefix = "",
  loading_arrow = FALSE,
  loading_taxa_num = 10,
  loading_text_color = "black",
  loading_arrow_color = "grey30",
  loading_text_size = 3,
  loading_text_italic = FALSE
)

Arguments

plot_type

default "point"; one or more elements of "point", "ellipse", "chull" and "centroid".

'point': add sample points
'ellipse': add confidence ellipse for points of each group
'chull': add convex hull for points of each group
'centroid': add centroid line of each group

color_values

default RColorBrewer::brewer.pal(8, "Dark2"); colors palette for different groups.

shape_values

default c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14); a vector for point shape types of groups, see ggplot2 tutorial.

plot_color

default NULL; a colname of sample_table to assign colors to different groups in plot.

plot_shape

default NULL; a colname of sample_table to assign shapes to different groups in plot.

plot_group_order

default NULL; a vector used to order the groups in the legend of plot.

add_sample_label

default NULL; a column name in sample_table; If provided, show the point name in plot.

point_size

default 3; point size when "point" is in plot_type parameter.

point_alpha

default .8; point transparency in plot when "point" is in plot_type parameter.

centroid_segment_alpha

default 0.6; segment transparency in plot when "centroid" is in plot_type parameter.

centroid_segment_size

default 1; segment size in plot when "centroid" is in plot_type parameter.

centroid_segment_linetype

default 3; the line type related with centroid in plot when "centroid" is in plot_type parameter.

ellipse_chull_fill

default TRUE; whether fill colors to the area of ellipse or chull.

ellipse_chull_alpha

default 0.1; color transparency in the ellipse or convex hull depending on whether "ellipse" or "centroid" is in plot_type parameter.

ellipse_level

default .9; confidence level of ellipse when "ellipse" is in plot_type parameter.

ellipse_type

default "t"; ellipse type when "ellipse" is in plot_type parameter; see type in stat_ellipse.

NMDS_stress_pos

default c(1, 1); a numerical vector with two values used to represent the insertion position of the stress text. The first one denotes the x-axis, while the second one corresponds to the y-axis. The assigned position is determined by multiplying the respective value with the maximum point on the corresponding coordinate axis. Thus, the x-axis position is equal to max(points of x axis) * NMDS_stress_pos[1], and the y-axis position is equal to max(points of y axis) * NMDS_stress_pos[2]. Negative values can also be utilized for the negative part of the axis. NMDS_stress_pos = NULL denotes no stress text to show.

NMDS_stress_text_prefix

default ""; If NMDS_stress_pos is not NULL, this parameter can be used to add text in front of the stress value.

loading_arrow

default FALSE; whether show the loading using arrow.

loading_taxa_num

default 10; the number of taxa used for the loading. Only available when loading_arrow = TRUE.

loading_text_color

default "black"; the color of taxa text. Only available when loading_arrow = TRUE.

loading_arrow_color

default "grey30"; the color of taxa arrow. Only available when loading_arrow = TRUE.

loading_text_size

default 3; the size of taxa text. Only available when loading_arrow = TRUE.

loading_text_italic

default FALSE; whether using italic for the taxa text. Only available when loading_arrow = TRUE.

Returns

ggplot.

Examples

t1$plot_ordination(plot_type = "point")
t1$plot_ordination(plot_color = "Group", plot_shape = "Group", plot_type = "point")
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "ellipse"))
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "centroid"), 
	  centroid_segment_linetype = 1)

Method `cal_manova()`

Calculate perMANOVA (Permutational Multivariate Analysis of Variance) based on the adonis2 function of vegan package <doi:10.1111/j.1442-9993.2001.01070.pp.x>.

Usage

trans_beta$cal_manova(
  manova_all = TRUE,
  manova_set = NULL,
  group = NULL,
  by_group = NULL,
  p_adjust_method = "fdr",
  ...
)

Arguments

manova_all: default TRUE; TRUE represents test for all the groups, i.e. the overall test; FALSE represents test for all the paired groups.
manova_set: default NULL; other specified group set for manova, such as "Group + Type" and "Group*Type". Please also see the formula parameter (only right-hand side) in adonis2 function of vegan package. The parameter manova_set has higher priority than manova_all parameter. If manova_set is provided; manova_all is disabled.
group: default NULL; a column name of sample_table used for manova. If NULL, search group variable stored in the object. Available when manova_set is not provided.
by_group: default NULL; one column name in sample_table; used to perform paired comparisions within each group. Only available when manova_all = FALSE and manova_set is not provided.
p_adjust_method: default "fdr"; p.adjust method; available when manova_all = FALSE; see method parameter of p.adjust function for available options.
...: parameters passed to adonis2 function of vegan package.

Returns

res_manova stored in object with data.frame class.

Examples

t1$cal_manova(manova_all = TRUE)

Method `cal_anosim()`

Analysis of similarities (ANOSIM) based on the anosim function of vegan package.

Usage

trans_beta$cal_anosim(
  paired = FALSE,
  group = NULL,
  by_group = NULL,
  p_adjust_method = "fdr",
  ...
)

Arguments

paired: default FALSE; whether perform paired test between any two combined groups from all the input groups.
group: default NULL; a column name of sample_table. If NULL, search group variable stored in the object.
by_group: default NULL; one column name in sample_table; used to perform paired comparisions within each group. Only available when paired = TRUE.
p_adjust_method: default "fdr"; p.adjust method; available when paired = TRUE; see method parameter of p.adjust function for available options.
...: parameters passed to anosim function of vegan package.

Returns

res_anosim stored in object with data.frame class.

Examples

t1$cal_anosim()

Method `cal_betadisper()`

Multivariate homogeneity test of groups dispersions (PERMDISP) based on betadisper function in vegan package.

Usage

trans_beta$cal_betadisper(...)

Arguments

...: parameters passed to betadisper function.

Returns

res_betadisper stored in object.

Examples

t1$cal_betadisper()

Method `cal_group_distance()`

Convert symmetric distance matrix to distance table of paired samples that are within groups or between groups.

Usage

trans_beta$cal_group_distance(
  within_group = TRUE,
  by_group = NULL,
  ordered_group = NULL,
  sep = " vs "
)

Arguments

within_group: default TRUE; whether obtain distance table of paired samples within groups; if FALSE, obtain distances of paired samples between any two groups.
by_group: default NULL; one colname name of sample_table in microtable object. If provided, transform distances by the provided by_group parameter. This is especially useful for ordering and filtering values further. When within_group = TRUE, the result of by_group parameter is the format of paired groups. When within_group = FALSE, the result of by_group parameter is the format same with the group information in sample_table.
ordered_group: default NULL; a vector representing the ordered elements of group parameter; only useful when within_group = FALSE.
sep: default TRUE; a character string to separate the group names after merging them into a new name.

Returns

res_group_distance stored in object.

Examples

\donttest{
t1$cal_group_distance(within_group = TRUE)
}

Method `cal_group_distance_diff()`

Differential test of converted distances across groups.

Usage

trans_beta$cal_group_distance_diff(
  group = NULL,
  by_group = NULL,
  by_ID = NULL,
  ...
)

Arguments

group: default NULL; a column name of object$res_group_distance used for the statistics; If NULL, use the group inside the object.
by_group: default NULL; a column of object$res_group_distance used to perform the differential test among elements in group parameter for each element in by_group parameter. So by_group has a larger scale than group parameter. This by_group is very different from the by_group parameter in the cal_group_distance function.
by_ID: default NULL; a column of object$res_group_distance used to perform paired t test or paired wilcox test for the paired data, such as the data of plant compartments for different plant species (ID). So by_ID should be the smallest unit of sample collection without any repetition in it.
...: parameters passed to cal_diff function of trans_alpha class.

Returns

res_group_distance_diff stored in object.

Examples

\donttest{
t1$cal_group_distance_diff()
}

Method `plot_group_distance()`

Plot the distances of paired groups within or between groups.

Usage

trans_beta$plot_group_distance(plot_group_order = NULL, ...)

Arguments

plot_group_order: default NULL; a vector used to order the groups in the plot.
...: parameters (except measure) passed to plot_alpha function of trans_alpha class.

Returns

ggplot.

Examples

\donttest{
t1$plot_group_distance()
}

Method `plot_clustering()`

Plot clustering result based on the ggdendro package.

Usage

trans_beta$plot_clustering(
  color_values = RColorBrewer::brewer.pal(8, "Dark2"),
  measure = NULL,
  group = NULL,
  replace_name = NULL
)

Arguments

color_values: default RColorBrewer::brewer.pal(8, "Dark2"); color palette for the text.
measure: default NULL; beta diversity index; If NULL, using the measure when creating object
group: default NULL; if provided, use this group to assign color.
replace_name: default NULL; if provided, use this as label.

Returns

ggplot.

Examples

t1$plot_clustering(group = "Group", replace_name = c("Saline", "Type"))

Method `clone()`

The objects of this class are cloneable with this method.

Usage

trans_beta$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `trans_beta$new`
## ------------------------------------------------

data(dataset)
t1 <- trans_beta$new(dataset = dataset, measure = "bray", group = "Group")

## ------------------------------------------------
## Method `trans_beta$cal_ordination`
## ------------------------------------------------

t1$cal_ordination(method = "PCoA")

## ------------------------------------------------
## Method `trans_beta$plot_ordination`
## ------------------------------------------------

t1$plot_ordination(plot_type = "point")
t1$plot_ordination(plot_color = "Group", plot_shape = "Group", plot_type = "point")
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "ellipse"))
t1$plot_ordination(plot_color = "Group", plot_type = c("point", "centroid"), 
	  centroid_segment_linetype = 1)

## ------------------------------------------------
## Method `trans_beta$cal_manova`
## ------------------------------------------------

t1$cal_manova(manova_all = TRUE)

## ------------------------------------------------
## Method `trans_beta$cal_anosim`
## ------------------------------------------------

t1$cal_anosim()

## ------------------------------------------------
## Method `trans_beta$cal_betadisper`
## ------------------------------------------------

t1$cal_betadisper()

## ------------------------------------------------
## Method `trans_beta$cal_group_distance`
## ------------------------------------------------


t1$cal_group_distance(within_group = TRUE)


## ------------------------------------------------
## Method `trans_beta$cal_group_distance_diff`
## ------------------------------------------------


t1$cal_group_distance_diff()


## ------------------------------------------------
## Method `trans_beta$plot_group_distance`
## ------------------------------------------------


t1$plot_group_distance()


## ------------------------------------------------
## Method `trans_beta$plot_clustering`
## ------------------------------------------------

t1$plot_clustering(group = "Group", replace_name = c("Saline", "Type"))

[Package microeco version 1.8.0 Index]

Create trans_beta object for beta-diversity analysis

Description

Methods

Public methods

Method new()

Usage

Arguments

Returns

Examples

Method cal_ordination()

Usage

Arguments

Returns

Examples

Method plot_ordination()

Usage

Arguments

Returns

Examples

Method cal_manova()

Usage

Arguments

Returns

Examples

Method cal_anosim()

Usage

Arguments

Returns

Examples

Method cal_betadisper()

Usage

Arguments

Returns

Examples

Method cal_group_distance()

Usage

Arguments

Returns

Examples

Method cal_group_distance_diff()

Usage

Arguments

Returns

Examples

Method plot_group_distance()

Usage

Arguments

Returns

Examples

Method plot_clustering()

Usage

Arguments

Returns

Examples

Method clone()

Usage

Arguments

Examples

Create `trans_beta` object for beta-diversity analysis

Method `new()`

Method `cal_ordination()`

Method `plot_ordination()`

Method `cal_manova()`

Method `cal_anosim()`

Method `cal_betadisper()`

Method `cal_group_distance()`

Method `cal_group_distance_diff()`

Method `plot_group_distance()`

Method `plot_clustering()`

Method `clone()`