trans_beta | R Documentation |
trans_beta
object for beta-diversity analysis based on the distance matrixThis class is a wrapper for a series of beta-diversity related analysis, including ordination calculation and plot 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.
new()
trans_beta$new(dataset = NULL, measure = NULL, group = NULL)
dataset
the object of microtable
class.
measure
default NULL; bray, jaccard, wei_unifrac or unwei_unifrac, or other name of matrix stored in microtable$beta_diversity
;
used for ordination, manova, group distance comparision, etc. The measure must be one of names in microtable$beta_diversity
list.
Please see cal_betadiv
function of microtable
class for more details.
group
default NULL; sample group used for manova, betadisper or group distance comparision.
parameters stored in the object.
data(dataset) t1 <- trans_beta$new(dataset = dataset, measure = "bray", group = "Group")
cal_ordination()
Unconstrained ordination.
trans_beta$cal_ordination( ordination = "PCoA", ncomp = 3, trans_otu = FALSE, scale_species = FALSE, ... )
ordination
default "PCoA"; "PCA", "PCoA" or "NMDS". PCA: principal component analysis; PCoA: principal coordinates analysis; NMDS: non-metric multidimensional scaling.
ncomp
default 3; dimensions needed in the result.
trans_otu
default FALSE; whether species abundance will be square transformed; only available when ordination = PCA
.
scale_species
default FALSE; whether species loading in PCA will be scaled.
...
parameters passed to vegan::rda
function when ordination = "PCA", or ape::pcoa
function when ordination = "PCoA",
or vegan::metaMDS
function when when ordination = "NMDS".
res_ordination
stored in the object.
t1$cal_ordination(ordination = "PCoA")
plot_ordination()
Plot the ordination result.
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" )
plot_type
default "point"; one or more elements of "point", "ellipse", "chull" and "centroid".
add point
add confidence ellipse for points of each group
add convex hull for points of each group
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
.
ggplot
.
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$plot_ordination(plot_color = "Group", plot_type = c("point", "centroid"), centroid_segment_linetype = 1)
cal_manova()
Calculate perMANOVA based on <doi:10.1111/j.1442-9993.2001.01070.pp.x> and R vegan adonis2
function.
trans_beta$cal_manova( manova_all = TRUE, manova_set = NULL, group = NULL, p_adjust_method = "fdr", ... )
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"
; see also adonis2
.
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.
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.
res_manova
stored in object.
t1$cal_manova(manova_all = TRUE)
cal_anosim()
Analysis of similarities (ANOSIM) based on R vegan anosim
function.
trans_beta$cal_anosim( group = NULL, paired = FALSE, p_adjust_method = "fdr", ... )
group
default NULL; a column name of sample_table
. If NULL, search group
variable stored in the object.
paired
default FALSE; whether perform paired test between any two combined groups from all the input groups.
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.
res_anosim
stored in object.
t1$cal_anosim()
cal_betadisper()
A wrapper for betadisper
function in vegan package for multivariate homogeneity test of groups dispersions.
trans_beta$cal_betadisper(...)
...
parameters passed to betadisper
function.
res_betadisper
stored in object.
t1$cal_betadisper()
cal_group_distance()
Convert sample distances within groups or between groups.
trans_beta$cal_group_distance( within_group = TRUE, by_group = NULL, ordered_group = NULL, sep = " vs " )
within_group
default TRUE; whether transform sample distance within groups, if FALSE, transform sample distance 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.
res_group_distance
stored in object.
\donttest{ t1$cal_group_distance(within_group = TRUE) }
cal_group_distance_diff()
Differential test of distances among groups.
trans_beta$cal_group_distance_diff( group = NULL, by_group = NULL, by_ID = NULL, ... )
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$res_group_distance
used to perform paired t test or paired wilcox test for the paired data,
such as the data of plant compartments for different plant species (ID).
So by_ID
should be the smallest unit of sample collection without any repetition in it.
...
parameters passed to cal_diff
function of trans_alpha
class.
res_group_distance_diff
stored in object.
\donttest{ t1$cal_group_distance_diff() }
plot_group_distance()
Plotting the distance between samples within or between groups.
trans_beta$plot_group_distance(plot_group_order = NULL, ...)
plot_group_order
default NULL; a vector used to order the groups in the plot.
...
parameters (except measure) passed to plot_alpha
function of trans_alpha
class.
ggplot
.
\donttest{ t1$plot_group_distance() }
plot_clustering()
Plotting clustering result based on the ggdendro
package.
trans_beta$plot_clustering( color_values = RColorBrewer::brewer.pal(8, "Dark2"), measure = NULL, group = NULL, replace_name = NULL )
color_values
default RColorBrewer::brewer.pal(8, "Dark2"); color palette for the text.
measure
default NULL; beta diversity index; If NULL, using the measure when creating object
group
default NULL; if provided, use this group to assign color.
replace_name
default NULL; if provided, use this as label.
ggplot
.
t1$plot_clustering(group = "Group", replace_name = c("Saline", "Type"))
clone()
The objects of this class are cloneable with this method.
trans_beta$clone(deep = FALSE)
deep
Whether to make a deep clone.
## ------------------------------------------------
## Method `trans_beta$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$plot_ordination(plot_type = "point")
t1$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"))
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