R: Create 'trans_alpha' object for alpha diversity statistics...

trans_alpha {microeco}

R Documentation

Create `trans_alpha` object for alpha diversity statistics and visualization.

Description

This class is a wrapper for a series of alpha diversity analysis, including the statistics and visualization.

Methods

Method `new()`

Usage

trans_alpha$new(
  dataset = NULL,
  group = NULL,
  by_group = NULL,
  by_ID = NULL,
  order_x = NULL
)

Arguments

dataset: an object of microtable class.
group: default NULL; a column name of sample_table used for the statistics.
by_group: default NULL; a column name of sample_table used to perform the differential test among groups (from group parameter) for each group (from by_group parameter) separately.
by_ID: default NULL; a column name of sample_table 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 in sample_table should be the smallest unit of sample collection without any repetition in it.
order_x: default NULL; a sample_table column name or a vector with sample names; if provided, order samples by using factor.

Returns

data_alpha and data_stat stored in the object.

Examples

\donttest{
data(dataset)
t1 <- trans_alpha$new(dataset = dataset, group = "Group")
}

Method `cal_diff()`

Differential test on alpha diversity.

Usage

trans_alpha$cal_diff(
  measure = NULL,
  method = c("KW", "KW_dunn", "wilcox", "t.test", "anova", "scheirerRayHare", "lm",
    "lme", "betareg", "glmm", "glmm_beta")[1],
  formula = NULL,
  p_adjust_method = "fdr",
  KW_dunn_letter = TRUE,
  alpha = 0.05,
  anova_post_test = "duncan.test",
  return_model = FALSE,
  ...
)

Arguments

measure

default NULL; character vector; If NULL, all indexes will be calculated; see names of microtable$alpha_diversity, e.g. c("Observed", "Chao1", "Shannon").

method

default "KW"; see the following available options:

'KW': Kruskal-Wallis Rank Sum Test for all groups (>= 2)
'KW_dunn': Dunn's Kruskal-Wallis Multiple Comparisons <10.1080/00401706.1964.10490181> based on dunnTest function in FSA package
'wilcox': Wilcoxon Rank Sum Test for all paired groups
't.test': Student's t-Test for all paired groups
'anova': Variance analysis. For one-way anova, the post hoc test is Duncan's new multiple range test based on duncan.test function of agricolae package. Please use anova_post_test parameter to change post hoc method. For multi-way anova, Please use formula parameter to specify the model and see aov for more details
'scheirerRayHare': Scheirer-Ray-Hare test (nonparametric test) for a two-way factorial experiment; see scheirerRayHare function of rcompanion package
'lm': Linear Model based on the lm function
'lme': Linear Mixed Effect Model based on the lmerTest package
'betareg': Beta Regression for Rates and Proportions based on the betareg package
'glmm': Generalized linear mixed model (GLMM) based on the glmmTMB package. A family function can be provided using parameter passing, such as: family = glmmTMB::lognormal(link = "log")
'glmm_beta': Generalized linear mixed model (GLMM) with a family function of beta distribution. This is an extension of the GLMM model in 'glmm' option. The only difference is in glmm_beta the family function is fixed with the beta distribution function, facilitating the fitting for proportional data (ranging from 0 to 1). The link function is fixed with "logit".

formula

default NULL; applied to two-way or multi-factor anova when method = "anova" or "scheirerRayHare" or "lme" or "betareg" or "glmm"; specified set for independent variables, i.e. the latter part of a general formula, such as 'block + N*P*K'.

p_adjust_method

default "fdr" (for "KW", "wilcox", "t.test" methods) or "holm" (for "KW_dunn"); P value adjustment method; For method = 'KW', 'wilcox' or 't.test', please see method parameter of p.adjust function for available options; For method = 'KW_dunn', please see dunn.test::p.adjustment.methods for available options.

KW_dunn_letter

default TRUE; For method = 'KW_dunn', TRUE denotes paired significances are presented by letters; FALSE means significances are shown by asterisk for paired comparison.

alpha

default 0.05; Significant level; used for generating significance letters when method is 'anova' or 'KW_dunn'.

anova_post_test

default "duncan.test". The post hoc test method for one-way anova. Other available options include "LSD.test" and "HSD.test". All those are the function names in agricolae package.

return_model

default FALSE; whether return the original lmer or glmm model list in the object.

...

parameters passed to kruskal.test (when method = "KW") or wilcox.test function (when method = "wilcox") or dunnTest function of FSA package (when method = "KW_dunn") or agricolae::duncan.test/agricolae::LSD.test/agricolae::HSD.test (when method = "anova", one-way anova) or rcompanion::scheirerRayHare (when method = "scheirerRayHare") or lmerTest::lmer (when method = "lme") or betareg::betareg (when method = "betareg") or glmmTMB::glmmTMB (when method = "glmm").

Returns

res_diff, stored in object with the format data.frame.
When method is "betareg", "lm", "lme" or "glmm", "Estimate" and "Std.Error" columns represent the fitted coefficient and its standard error, respectively.

Examples

\donttest{
t1$cal_diff(method = "KW")
t1$cal_diff(method = "anova")
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "anova")
}

Method `plot_alpha()`

Plot the alpha diversity. Box plot is used for the visualization of alpha diversity when the group is found in the object. Heatmap is employed automatically to show the significances of differential test when the formula is found in the res_diff table in the object.

Usage

trans_alpha$plot_alpha(
  color_values = RColorBrewer::brewer.pal(8, "Dark2"),
  measure = "Shannon",
  group = NULL,
  add_sig = TRUE,
  add_sig_label = "Significance",
  add_sig_text_size = 3.88,
  add_sig_label_num_dec = 4,
  boxplot_add = "jitter",
  order_x_mean = FALSE,
  y_start = 0.1,
  y_increase = 0.05,
  xtext_angle = 30,
  xtext_size = 13,
  ytitle_size = 17,
  barwidth = 0.9,
  use_boxplot = TRUE,
  plot_SE = TRUE,
  errorbar_size = 1,
  errorbar_width = 0.2,
  point_size = 3,
  point_alpha = 0.8,
  add_line = FALSE,
  line_size = 0.8,
  line_type = 2,
  line_color = "grey50",
  line_alpha = 0.5,
  heatmap_cell = "P.unadj",
  heatmap_sig = "Significance",
  heatmap_x = "Factors",
  heatmap_y = "Measure",
  heatmap_lab_fill = "P value",
  coefplot_sig_pos = 2,
  ...
)

Arguments

color_values: default RColorBrewer::brewer.pal(8, "Dark2"); color pallete for groups.
measure: default "Shannon"; one alpha diversity index in the object.
group: default NULL; group name used for the plot.
add_sig: default TRUE; wheter add significance label using the result of cal_diff function, i.e. object$res_diff; This is manily designed to add post hoc test of anova or other significances to make the label mapping easy.
add_sig_label: default "Significance"; select a colname of object$res_diff for the label text when 'Letter' is not in the table, such as 'P.adj' or 'Significance'.
add_sig_text_size: default 3.88; the size of text in added label.
add_sig_label_num_dec: default 4; reserved decimal places when the parameter add_sig_label use numeric column, like 'P.adj'.
boxplot_add: default "jitter"; points type, see the add parameter in ggpubr::ggboxplot.
order_x_mean: default FALSE; whether order x axis by the means of groups from large to small.
y_start: default 0.1; the y axis value from which to add the significance asterisk label; the default 0.1 means max(values) + 0.1 * (max(values) - min(values)).
y_increase: default 0.05; the increasing y axia space to add the label (asterisk or letter); the default 0.05 means 0.05 * (max(values) - min(values)); this parameter is also used to label the letters of anova result with the fixed space.
xtext_angle: default 30; number (e.g. 30) used to make x axis text generate angle.
xtext_size: default 13; x axis text size. NULL means the default size in ggplot2.
ytitle_size: default 17; y axis title size.
barwidth: default 0.9; the bar width in plot; applied when by_group is not NULL.
use_boxplot: default TRUE; TRUE denotes boxplot by using the data_alpha table in the object. FALSE represents mean-sd or mean-se plot by invoking the data_stat table in the object.
plot_SE: default TRUE; TRUE: the errorbar is mean±se; FALSE: the errorbar is mean±sd.
errorbar_size: default 1; errorbar size. Available when use_boxplot = FALSE.
errorbar_width: default 0.2; errorbar width. Available when use_boxplot = FALSE and by_group is NULL.
point_size: default 3; point size for taxa. Available when use_boxplot = FALSE.
point_alpha: default 0.8; point transparency. Available when use_boxplot = FALSE.
add_line: default FALSE; whether add line. Available when use_boxplot = FALSE.
line_size: default 0.8; line size when add_line = TRUE. Available when use_boxplot = FALSE.
line_type: default 2; an integer; line type when add_line = TRUE. Available when use_boxplot = FALSE.
line_color: default "grey50"; line color when add_line = TRUE. Available when use_boxplot = FALSE and by_group is NULL.
line_alpha: default 0.5; line transparency when add_line = TRUE. Available when use_boxplot = FALSE.
heatmap_cell: default "P.unadj"; the column of res_diff table for the cell of heatmap when formula with multiple factors is found in the method.
heatmap_sig: default "Significance"; the column of res_diff for the significance label of heatmap.
heatmap_x: default "Factors"; the column of res_diff for the x axis of heatmap.
heatmap_y: default "Taxa"; the column of res_diff for the y axis of heatmap.
heatmap_lab_fill: default "P value"; legend title of heatmap.
coefplot_sig_pos: default 2; Significance label position in the coefficient point and errorbar plot. The formula is Estimate + coefplot_sig_pos * Std.Error. This plot is used when there is only one measure found in the table, and 'Estimate' and 'Std.Error' are both in the column names (such as for lm and lme methods). The x axis is 'Estimate', and y axis denotes 'Factors'. When coefplot_sig_pos is a negative value, the label is in the left of the errorbar. Errorbar size and width in the coefficient point plot can be adjusted with the parameters errorbar_size and errorbar_width. Point size and alpha can be adjusted with parameters point_size and point_alpha. The significance label size can be adjusted with parameter add_sig_text_size. Furthermore, the vertical line around 0 can be adjusted with parameters line_size, line_type, line_color and line_alpha.
...: parameters passing to ggpubr::ggboxplot function when box plot is used or plot_cor function in trans_env class for the heatmap of multiple factors when formula is found in the res_diff of the object.

Returns

ggplot.

Examples

\donttest{
t1 <- trans_alpha$new(dataset = dataset, group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)
}

Method `print()`

Print the trans_alpha object.

Usage

trans_alpha$print()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

trans_alpha$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `trans_alpha$new`
## ------------------------------------------------


data(dataset)
t1 <- trans_alpha$new(dataset = dataset, group = "Group")


## ------------------------------------------------
## Method `trans_alpha$cal_diff`
## ------------------------------------------------


t1$cal_diff(method = "KW")
t1$cal_diff(method = "anova")
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "anova")


## ------------------------------------------------
## Method `trans_alpha$plot_alpha`
## ------------------------------------------------


t1 <- trans_alpha$new(dataset = dataset, group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)
t1 <- trans_alpha$new(dataset = dataset, group = "Type", by_group = "Group")
t1$cal_diff(method = "wilcox")
t1$plot_alpha(measure = "Shannon", add_sig = TRUE)

[Package microeco version 1.8.0 Index]

Create trans_alpha object for alpha diversity statistics and visualization.

Description

Methods

Public methods

Method new()

Usage

Arguments

Returns

Examples

Method cal_diff()

Usage

Arguments

Returns

Examples

Method plot_alpha()

Usage

Arguments

Returns

Examples

Method print()

Usage

Method clone()

Usage

Arguments

Examples

Create `trans_alpha` object for alpha diversity statistics and visualization.

Method `new()`

Method `cal_diff()`

Method `plot_alpha()`

Method `print()`

Method `clone()`