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microeco (version 0.7.1)

trans_beta: Create trans_beta object for the analysis of distance matrix of beta-diversity.

Description

This class is a wrapper for a series of beta-diversity related analysis, including several ordination calculations and plotting 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> and PERMDISP. Please also cite the original paper: An et al. (2019). Soil bacterial community structure in Chinese wetlands. Geoderma, 337, 290-299.

Arguments

Methods

Public methods

Method new()

Usage

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

Arguments

dataset

the object of microtable Class.

measure

default NULL; bray, jaccard, wei_unifrac or unwei_unifrac, or other name of matrix you add; beta diversity index used for ordination, manova or group distance.

group

default NULL; sample group used for manova, betadisper or group distance.

Returns

parameters stored in the object.

Examples

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

Method cal_ordination()

Ordination based on An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035>.

Usage

trans_beta$cal_ordination(
  ordination = "PCoA",
  ncomp = 3,
  trans_otu = FALSE,
  scale_species = FALSE
)

Arguments

ordination

default "PCoA"; "PCA", "PCoA" or "NMDS".

ncomp

default 3; the returned dimensions.

trans_otu

default FALSE; whether species abundance will be square transformed, used for PCA.

scale_species

default FALSE; whether species loading in PCA will be scaled.

Returns

res_ordination stored in the object.

Examples

t1$cal_ordination(ordination = "PCoA")

Method plot_ordination()

Plotting the ordination result based on An et al. (2019) <doi:10.1016/j.geoderma.2018.09.035>.

Usage

trans_beta$plot_ordination(
  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,
  plot_point_size = 3,
  plot_point_alpha = 0.9,
  plot_sample_label = NULL,
  plot_group_centroid = FALSE,
  plot_group = NULL,
  segment_alpha = 0.6,
  centroid_linetype = 3,
  plot_group_ellipse = FALSE,
  ellipse_level = 0.9,
  ellipse_alpha = 0.1,
  ellipse_type = "t"
)

Arguments

color_values

default RColorBrewer::brewer.pal(8, "Dark2"); colors for presentation.

shape_values

default c(16, 17, 7, 8, 15, 18, 11, 10, 12, 13, 9, 3, 4, 0, 1, 2, 14); a vector used in the shape type, see ggplot2 tutorial.

plot_color

default NULL; the sample group name used for color in plot.

plot_shape

default NULL; the sample group name used for shape in plot.

plot_group_order

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

plot_point_size

default 3; point size in plot.

plot_point_alpha

default .9; point transparency in plot.

plot_sample_label

default NULL; the column name in sample table, if provided, show the point name in plot.

plot_group_centroid

default FALSE; whether show the centroid in each group of plot.

plot_group

default NULL; the column name in sample table, generally used with plot_group_centroid and plot_group_ellipse.

segment_alpha

default .6; segment transparency in plot.

centroid_linetype

default 3; the line type related with centroid in plot.

plot_group_ellipse

default FALSE; whether show the confidence ellipse in each group of plot.

ellipse_level

default .9; confidence level of ellipse.

ellipse_alpha

default .1; color transparency in the ellipse.

ellipse_type

default t; see type in stat_ellipse.

Returns

ggplot.

Examples

t1$plot_ordination(plot_color = "Group", plot_shape = "Group", plot_group_ellipse = TRUE)

Method cal_manova()

Calculate perMANOVA based on Anderson al. (2008) <doi:10.1111/j.1442-9993.2001.01070.pp.x> and R vegan adonis function.

Usage

trans_beta$cal_manova(
  cal_manova_all = FALSE,
  cal_manova_paired = FALSE,
  cal_manova_set = NULL,
  permutations = 999
)

Arguments

cal_manova_all

default FALSE; whether manova is used for all data.

cal_manova_paired

default FALSE; whether manova is used for all the paired groups.

cal_manova_set

default NULL; specified group set for manova, see adonis.

permutations

default 999; see permutations in adonis.

Returns

res_manova stored in object.

Examples

t1$cal_manova(cal_manova_all = TRUE)

Method cal_betadisper()

A wrapper for betadisper function in vegan package for multivariate homogeneity test of groups dispersions.

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()

Transform sample distances within groups or between groups.

Usage

trans_beta$cal_group_distance(within_group = TRUE)

Arguments

within_group

default TRUE; whether transform sample distance within groups, if FALSE, transform sample distance between any two groups.

Returns

res_group_distance stored in object.

Examples

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

Method plot_group_distance()

Plotting the distance between samples within or between groups.

Usage

trans_beta$plot_group_distance(
  plot_group_order = NULL,
  color_values = RColorBrewer::brewer.pal(8, "Dark2"),
  distance_pair_stat = FALSE,
  hide_ns = FALSE,
  hide_ns_more = NULL,
  pair_compare_filter_match = NULL,
  pair_compare_filter_select = NULL,
  pair_compare_method = "wilcox.test",
  plot_distance_xtype = NULL
)

Arguments

plot_group_order

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

color_values

colors for presentation.

distance_pair_stat

default FALSE; whether do the paired comparisions.

hide_ns

default FALSE; whether hide the "ns" pairs, i.e. non significant comparisions.

hide_ns_more

default NULL; character vector; available when hide_ns = TRUE; if provided, used for the specific significance filtering, such as c("ns", "*").

pair_compare_filter_match

default NULL; only available when hide_ns = FALSE; if provided, remove the matched groups; use the regular express to match the paired groups.

pair_compare_filter_select

default NULL; numeric vector;only available when hide_ns = FALSE; if provided, only select those input groups. This parameter must be a numeric vector used to select the paired combination of groups. For example, pair_compare_filter_select = c(1, 3) can be used to select "CW"-"IW" and "IW"-"TW" from all the three pairs "CW"-"IW", "CW"-"TW" and "IW"-"TW" of ordered groups ("CW", "IW", "TW"). The parameter pair_compare_filter_select and pair_compare_filter_match can not be both used together.

pair_compare_method

default wilcox.test; wilcox.test, kruskal.test, t.test or anova.

plot_distance_xtype

default NULL; number used to make x axis text generate angle.

Returns

ggplot.

Examples

\donttest{
t1$plot_group_distance(distance_pair_stat = TRUE)
t1$plot_group_distance(distance_pair_stat = TRUE, hide_ns = TRUE)
t1$plot_group_distance(distance_pair_stat = TRUE, hide_ns = TRUE, hide_ns_more = c("ns", "*"))
t1$plot_group_distance(distance_pair_stat = TRUE, pair_compare_filter_select = 3)
}

Method plot_clustering()

Plotting clustering result. Require ggdendro package.

Usage

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

Arguments

use_colors

colors for presentation.

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 print()

Print the trans_beta object.

Usage

trans_beta$print()

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

Run this code
# NOT RUN {
## ------------------------------------------------
## Method `trans_beta$new`
## ------------------------------------------------

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

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

t1$cal_ordination(ordination = "PCoA")		

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

t1$plot_ordination(plot_color = "Group", plot_shape = "Group", plot_group_ellipse = TRUE)

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

t1$cal_manova(cal_manova_all = TRUE)

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

t1$cal_betadisper()

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

# }
# NOT RUN {
t1$cal_group_distance(within_group = TRUE)
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_beta$plot_group_distance`
## ------------------------------------------------

# }
# NOT RUN {
t1$plot_group_distance(distance_pair_stat = TRUE)
t1$plot_group_distance(distance_pair_stat = TRUE, hide_ns = TRUE)
t1$plot_group_distance(distance_pair_stat = TRUE, hide_ns = TRUE, hide_ns_more = c("ns", "*"))
t1$plot_group_distance(distance_pair_stat = TRUE, pair_compare_filter_select = 3)
# }
# NOT RUN {
## ------------------------------------------------
## Method `trans_beta$plot_clustering`
## ------------------------------------------------

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

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