taxaturn {mecoturn} | R Documentation |
Analyze the 'turnover' of taxa along a defined gradient. The workflow of taxaturn class includes the taxonomic abundance calculation, abundance transformation, abundance change summary, statistical analysis and visualization.
new()
taxaturn$new( dataset, taxa_level = "Phylum", group, ordered_group, by_ID = NULL, by_group = NULL, filter_thres = 0 )
dataset
the object of microtable
class.
taxa_level
default "Phylum"; taxonomic rank name, such as "Genus". An integer is also acceptable.
If the provided taxa_level is not found in taxa_abund
list,
the function will invoke the cal_abund
function to obtain the relative abudance automatically.
group
sample group used for the selection; a colname of input microtable$sample_table
.
ordered_group
a vector representing the ordered elements of group
parameter.
by_ID
default NULL; a column of sample_table used to obtain the consistent change along provided elements. So by_ID can be ID (unique repetition) or even group (with repetitions). If it denotes unique ID, consistent change can be performed across each ID. It is also especially useful for the paired wilcox test (or paired t test) in the following analysis. If it does not represent unique ID, the mean of each group will be calculated, and consistent change across groups will be obtained.
by_group
default NULL; NULL or other colname of sample_table of input dataset used to show the result for different groups;
NULL represents the output is the default consistent change across all the elements in by_ID
;
a colname of sample_table of input dataset means the consistent change is obtained for each group instead of all the elements in by_group
;
Note that the by_group can be same with by_ID, in which the final change is the result of each element in by_group
.
So generally by_group
has a larger scale than by_ID
parameter in terms of the sample numbers in each element.
filter_thres
default 0; the mean abundance threshold used to filter features with low abudance.
res_abund
, res_change_pair
and res_change
in the object:
res_abund
The Mean, SD or SE of abundances for all the samples or each group. Mean: mean of abudances; SD: standard deviation; SE: standard error.
res_change_pair
The difference value of abudances between two niches, i.e. the latter minus the former.
res_change
The summary of the abudance change results in res_change_pair.
data(wheat_16S) t1 <- taxaturn$new(wheat_16S, taxa_level = "Phylum", group = "Type", ordered_group = c("S", "RS", "R"), by_ID = "Plant_ID", filter_thres = 0.01)
cal_diff()
Differential test of taxonomic abundance across groups
taxaturn$cal_diff( method = c("wilcox", "t.test", "anova", "betareg", "lme", "glmm")[1], group2num = FALSE, ... )
method
default "wilcox"; see the following available options:
Wilcoxon Rank Sum and Signed Rank Tests for all paired groups
Student's t-Test for all paired groups
one-way or multi-way anova
Beta Regression based on the betareg
package
lme: Linear Mixed Effect Model based on the lmerTest
package
Generalized linear mixed model (GLMM) based on the glmmTMB
package with the beta family function,
i.e. family = glmmTMB::beta_family(link = "logit")
.
For more parameters, please see glmmTMB::glmmTMB
function.
In the return table, Conditional_R2 and Marginal_R2 represent total variance (explained by both fixed and random effects) and the variance explained by
fixed effects, respectively. The significance of fixed factors are tested by Chi-square test from function car::Anova
.
The significance of 'Estimate' in each term of fixed factors comes from the model.
group2num
default FALSE; whether convert ordered groups to integer numbers when method is "lme" or "glmm".
...
parameters passed to trans_diff$new
.
res_change
or res_diff
in the object.
t1$cal_diff(method = "wilcox")
plot()
Plot the line chart.
taxaturn$plot( select_taxon = NULL, color_values = RColorBrewer::brewer.pal(8, "Dark2"), delete_prefix = TRUE, plot_type = c("point", "line", "errorbar", "smooth")[1:3], errorbar_SE = TRUE, rect_fill = TRUE, rect_color = c("grey70", "grey90"), rect_alpha = 0.2, position = position_dodge(0.1), errorbar_size = 1, errorbar_width = 0.1, point_size = 3, point_alpha = 0.8, line_size = 0.8, line_alpha = 0.8, line_type = 1, ... )
select_taxon
default NULL; a taxon name.
Note that if delete_prefix
is TRUE, the provided select_taxon should be taxa names without long prefix (those before |);
if delete_prefix
is FALSE, the select_taxon should be full names same with those in the res_abund
of the object.
color_values
default RColorBrewer::brewer.pal
(8, "Dark2"); colors palette for the plotting.
delete_prefix
default TRUE; whether delete the prefix in the taxa names.
plot_type
default c("point", "line", "errorbar", "smooth")[1:3]; a vector of visualization types. Multiple elements are available.
'smooth' denotes the fitting with geom_smooth
function of ggplot2 package.
errorbar_SE
default TRUE; TRUE: plot the errorbar with mean ± se; FALSE: plot the errorbar with mean ± sd.
rect_fill
default TRUE; Whether fill color in each rectangular area.
rect_color
default c("grey70", "grey90"); the colors used to fill different rectangular area.
rect_alpha
default 0.2; the fill color transparency in rectangular area.
position
default position_dodge(0.1); Position adjustment for the points and lines, either as a string (such as "identity"), or the result of a call to a position adjustment function.
errorbar_size
default 1; errorbar size.
errorbar_width
default 0.1; errorbar width.
point_size
default 3; point size for taxa.
point_alpha
default 0.8; point transparency.
line_size
default 0.8; line size.
line_alpha
default 0.8; line transparency.
line_type
default 1; an integer; line type.
...
parameters passed to geom_smooth
when 'smooth' is in plot_type parameter.
ggplot2 plot.
t1$plot()
clone()
The objects of this class are cloneable with this method.
taxaturn$clone(deep = FALSE)
deep
Whether to make a deep clone.
## ------------------------------------------------
## Method `taxaturn$new`
## ------------------------------------------------
data(wheat_16S)
t1 <- taxaturn$new(wheat_16S, taxa_level = "Phylum", group = "Type",
ordered_group = c("S", "RS", "R"), by_ID = "Plant_ID", filter_thres = 0.01)
## ------------------------------------------------
## Method `taxaturn$cal_diff`
## ------------------------------------------------
t1$cal_diff(method = "wilcox")
## ------------------------------------------------
## Method `taxaturn$plot`
## ------------------------------------------------
t1$plot()