betaturn {mecoturn} | R Documentation |
Analyze the 'turnover' of microbial communities, i.e. beta-diversity along a gradient <doi:10.1111/j.1461-0248.2010.01552.x>. The workflow consists of the steps of dissimilarity matrix generation, matrix conversion, differential test and visualization.
new()
betaturn$new( dataset, measure = "bray", filter_thres = 0, abundance.weighted = TRUE, null.model = NULL, runs = 1000, iterations = 1000, ... )
dataset
the object of microtable
class.
measure
default "bray"; beta diversity dissimilarity metric;
must be one of c("bray", "jaccard", "wei_unifrac", "unwei_unifrac", "betaMPD", "betaMNTD", "betaNRI", "betaNTI", "ses_UniFrac", "RCbray")
or other options in parameter method
of vegan::vegdist
function.
If the distance matrix has been in the beta_diversity list of microtable object,
the function can ignore this step. Otherwise, the function can generate the corresponding beta diversity distance matrix in the microtable object.
bray: Bray-Curtis; RCbray: Raup–Crick based Bray-Curtis; wei_unifrac: weighted UniFrac; ses_UniFrac: standardized deviation of UniFrac.
filter_thres
default 0; the relative abundance threshold used to filter features with low abundance.
abundance.weighted
default TRUE; whether use abundance-weighted method for the phylogenetic metrics.
null.model
default NULL; one of c("taxa.labels", "richness", "frequency", "sample.pool", "phylogeny.pool", "independentswap", "trialswap")
,
in which "taxa.labels" can only be used for phylogenetic analysis.
See null.model
parameter of ses.mntd
function in picante
package for the algorithm details.
runs
default 1000; simulation number of times for null model.
iterations
default 1000; iteration number for part null models to perform; see iterations parameter of picante::randomizeMatrix
function.
...
parameters passed to cal_betadiv
function of microtable
class when provided measure is not in the current vector;
parameters passed to cal_betamntd
function of trans_nullmodel
class when measure = "betaMNTD"
;
parameters passed to cal_ses_betamntd
function of trans_nullmodel
class when measure = "betaNTI"
.
dataset
, stored in the object. The new dataset has a beta_diversity list and the calculated distance matrix in the list.
data(wheat_16S) b1 <- betaturn$new(wheat_16S, measure = "bray")
cal_group_distance()
Convert sample distances within groups or between groups.
betaturn$cal_group_distance( group, within_group = TRUE, by_group = NULL, ordered_group = NULL, sep = " vs ", add_cols = NULL )
group
one colname of sample_table in microtable
object used for group distance convertion.
within_group
default TRUE; whether transform sample distance within groups? If FALSE, transform sample distances between any two groups.
by_group
default NULL; one colname of sample_table in microtable
object.
If provided, convert distances according to the provided by_group parameter. This is especially useful for ordering and filtering values further.
When within_group = TRUE
, the result of by_group parameter is the format of paired groups.
When within_group = FALSE
, the result of by_group parameter is the format same with the group information in sample_table
.
ordered_group
default NULL; a vector representing the ordered elements of group
parameter; only useful when within_group = FALSE.
sep
default TRUE; a character string to separate the group names after merging them into a new name.
add_cols
default NULL; add several columns of sample_table to the final res_group_distance
table according to the by_group
column;
invoked only when within_group = FALSE
.
res_group_distance
stored in object.
b1$cal_group_distance(group = "Type", within_group = FALSE, by_group = "Plant_ID")
cal_group_distance_diff()
Differential test of distances among groups.
betaturn$cal_group_distance_diff(...)
...
parameters passed to cal_group_distance_diff
function of trans_beta
class.
res_group_distance_diff
stored in object.
b1$cal_group_distance_diff(method = "wilcox")
plot_group_distance()
Plot the distance between samples within or between groups.
betaturn$plot_group_distance(...)
...
parameters passed to plot_group_distance
function of trans_beta
class.
ggplot
.
b1$plot_group_distance()
clone()
The objects of this class are cloneable with this method.
betaturn$clone(deep = FALSE)
deep
Whether to make a deep clone.
## ------------------------------------------------
## Method `betaturn$new`
## ------------------------------------------------
data(wheat_16S)
b1 <- betaturn$new(wheat_16S, measure = "bray")
## ------------------------------------------------
## Method `betaturn$cal_group_distance`
## ------------------------------------------------
b1$cal_group_distance(group = "Type", within_group = FALSE, by_group = "Plant_ID")
## ------------------------------------------------
## Method `betaturn$cal_group_distance_diff`
## ------------------------------------------------
b1$cal_group_distance_diff(method = "wilcox")
## ------------------------------------------------
## Method `betaturn$plot_group_distance`
## ------------------------------------------------
b1$plot_group_distance()