trans_nullmodel: Create trans_nullmodel object for phylogeny- and...

trans_nullmodelR Documentation

Create trans_nullmodel object for phylogeny- and taxonomy-based null model analysis.

Description

This class is a wrapper for a series of null model related approaches, including the mantel correlogram analysis of phylogenetic signal, beta nearest taxon index (betaNTI), beta net relatedness index (betaNRI), NTI, NRI and RCbray calculations; See Stegen et al. (2013) <10.1038/ismej.2013.93> and Liu et al. (2017) <doi:10.1038/s41598-017-17736-w> for the algorithms and applications.

Methods

Public methods


Method new()

Usage
trans_nullmodel$new(
  dataset = NULL,
  filter_thres = 0,
  taxa_number = NULL,
  group = NULL,
  select_group = NULL,
  env_cols = NULL,
  add_data = NULL,
  complete_na = FALSE
)
Arguments
dataset

the object of microtable Class.

filter_thres

default 0; the relative abundance threshold.

taxa_number

default NULL; how many taxa the user want to keep, if provided, filter_thres parameter will be forcible invalid.

group

default NULL; which group column name in sample_table is selected.

select_group

default NULL; the group name, used following the group to filter samples.

env_cols

default NULL; number or name vector to select the environmental data in dataset$sample_table.

add_data

default NULL; provide environmental data table additionally.

complete_na

default FALSE; whether fill the NA in environmental data based on the method in mice package.

Returns

data_comm and data_tree in object.

Examples
data(dataset)
data(env_data_16S)
t1 <- trans_nullmodel$new(dataset, filter_thres = 0.0005, add_data = env_data_16S)

Method cal_mantel_corr()

Calculate mantel correlogram.

Usage
trans_nullmodel$cal_mantel_corr(
  use_env = NULL,
  break.pts = seq(0, 1, 0.02),
  cutoff = FALSE,
  ...
)
Arguments
use_env

default NULL; numeric or character vector to select env_data; if provide multiple variables or NULL, use PCA (principal component analysis) to reduce dimensionality.

break.pts

default seq(0, 1, 0.02); see break.pts parameter in mantel.correlog of vegan package.

cutoff

default FALSE; see cutoff parameter in mantel.correlog.

...

parameters pass to mantel.correlog

Returns

res_mantel_corr in object.

Examples
\donttest{
t1$cal_mantel_corr(use_env = "pH")
}

Method plot_mantel_corr()

Plot mantel correlogram.

Usage
trans_nullmodel$plot_mantel_corr(point_shape = 22, point_size = 3)
Arguments
point_shape

default 22; the number for selecting point shape type; see ggplot2 manual for the number meaning.

point_size

default 3; the point size.

Returns

ggplot.

Examples
\donttest{
t1$plot_mantel_corr()
}

Method cal_betampd()

Calculate betaMPD (mean pairwise distance). Same with comdist in picante package, but faster.

Usage
trans_nullmodel$cal_betampd(abundance.weighted = TRUE)
Arguments
abundance.weighted

default TRUE; whether use abundance-weighted method.

Returns

res_betampd in object.

Examples
\donttest{
t1$cal_betampd(abundance.weighted = TRUE)
}

Method cal_betamntd()

Calculate betaMNTD (mean nearest taxon distance). Same with comdistnt in picante package, but faster.

Usage
trans_nullmodel$cal_betamntd(
  abundance.weighted = TRUE,
  exclude.conspecifics = FALSE,
  use_iCAMP = FALSE,
  use_iCAMP_force = TRUE,
  iCAMP_tempdir = NULL,
  ...
)
Arguments
abundance.weighted

default TRUE; whether use abundance-weighted method.

exclude.conspecifics

default FALSE; see exclude.conspecifics parameter in comdistnt function of picante package.

use_iCAMP

default FALSE; whether use bmntd.big function of iCAMP package to calculate betaMNTD. This method can store the phylogenetic distance matrix on the disk to lower the memory spending and perform the calculation parallelly.

use_iCAMP_force

default FALSE; whether use bmntd.big function of iCAMP package automatically when the feature number is large.

iCAMP_tempdir

default NULL; the temporary directory used to place the large tree file; If NULL; use the system user tempdir.

...

paremeters pass to iCAMP::pdist.big function.

Returns

res_betamntd in object.

Examples
\donttest{
t1$cal_betamntd(abundance.weighted = TRUE)
}

Method cal_ses_betampd()

Calculate standardized effect size of betaMPD, i.e. beta net relatedness index (betaNRI).

Usage
trans_nullmodel$cal_ses_betampd(
  runs = 1000,
  null.model = c("taxa.labels", "richness", "frequency", "sample.pool",
    "phylogeny.pool", "independentswap", "trialswap")[1],
  abundance.weighted = TRUE,
  iterations = 1000
)
Arguments
runs

default 1000; simulation runs.

null.model

default "taxa.labels"; The available options include "taxa.labels", "richness", "frequency", "sample.pool", "phylogeny.pool", "independentswap"and "trialswap"; see null.model parameter of ses.mntd function in picante package for the algorithm details.

abundance.weighted

default TRUE; whether use weighted abundance.

iterations

default 1000; iteration number for part null models to perform; see iterations parameter of picante::randomizeMatrix function.

Returns

res_ses_betampd in object.

Examples
\donttest{
# only run 50 times for the example; default 1000
t1$cal_ses_betampd(runs = 50, abundance.weighted = TRUE)
}

Method cal_ses_betamntd()

Calculate standardized effect size of betaMNTD, i.e. beta nearest taxon index (betaNTI).

Usage
trans_nullmodel$cal_ses_betamntd(
  runs = 1000,
  null.model = c("taxa.labels", "richness", "frequency", "sample.pool",
    "phylogeny.pool", "independentswap", "trialswap")[1],
  abundance.weighted = TRUE,
  exclude.conspecifics = FALSE,
  use_iCAMP = FALSE,
  use_iCAMP_force = TRUE,
  iCAMP_tempdir = NULL,
  nworker = 2,
  iterations = 1000
)
Arguments
runs

default 1000; simulation number of null model.

null.model

default "taxa.labels"; The available options include "taxa.labels", "richness", "frequency", "sample.pool", "phylogeny.pool", "independentswap"and "trialswap"; see null.model parameter of ses.mntd function in picante package for the algorithm details.

abundance.weighted

default TRUE; whether use abundance-weighted method.

exclude.conspecifics

default FALSE; see comdistnt in picante package.

use_iCAMP

default FALSE; whether use bmntd.big function of iCAMP package to calculate betaMNTD. This method can store the phylogenetic distance matrix on the disk to lower the memory spending and perform the calculation parallelly.

use_iCAMP_force

default FALSE; whether to make use_iCAMP to be TRUE when the feature number is large.

iCAMP_tempdir

default NULL; the temporary directory used to place the large tree file; If NULL; use the system user tempdir.

nworker

default 2; the CPU thread number.

iterations

default 1000; iteration number for part null models to perform; see iterations parameter of picante::randomizeMatrix function.

Returns

res_ses_betamntd in object.

Examples
\donttest{
# only run 50 times for the example; default 1000
t1$cal_ses_betamntd(runs = 50, abundance.weighted = TRUE, exclude.conspecifics = FALSE)
}

Method cal_rcbray()

Calculate Bray–Curtis-based Raup–Crick (RCbray).

Usage
trans_nullmodel$cal_rcbray(
  runs = 1000,
  verbose = TRUE,
  null.model = "independentswap"
)
Arguments
runs

default 1000; simulation runs.

verbose

default TRUE; whether show the calculation process message.

null.model

default "independentswap"; see more available options in randomizeMatrix function of picante package.

Returns

res_rcbray in object.

Examples
\donttest{
# only run 50 times for the example; default 1000
t1$cal_rcbray(runs = 50)
}

Method cal_process()

Infer the ecological processes according to ses.betaMNTD ses.betaMPD and rcbray.

Usage
trans_nullmodel$cal_process(use_betamntd = TRUE)
Arguments
use_betamntd

default TRUE; whether use ses.betaMNTD; if false, use ses.betaMPD.

Returns

res_rcbray in object.

Examples
\donttest{
t1$cal_process(use_betamntd = TRUE)
}

Method cal_NRI()

Calculates Nearest Relative Index (NRI), equivalent to -1 times the standardized effect size of MPD.

Usage
trans_nullmodel$cal_NRI(
  null.model = "taxa.labels",
  abundance.weighted = FALSE,
  runs = 999,
  ...
)
Arguments
null.model

default "taxa.labels"; Null model to use; see null.model parameter in ses.mpd function of picante package for available options.

abundance.weighted

default FALSE; Should mean nearest relative distances for each species be weighted by species abundance?

runs

default 999; Number of randomizations.

...

paremeters pass to ses.mpd function in picante package.

Returns

res_NRI in object, equivalent to -1 times ses.mpd.

Examples
\donttest{
# only run 50 times for the example; default 999
t1$cal_NRI(null.model = "taxa.labels", abundance.weighted = FALSE, runs = 50)
}

Method cal_NTI()

Calculates Nearest Taxon Index (NTI), equivalent to -1 times the standardized effect size of MNTD.

Usage
trans_nullmodel$cal_NTI(
  null.model = "taxa.labels",
  abundance.weighted = FALSE,
  runs = 999,
  ...
)
Arguments
null.model

default "taxa.labels"; Null model to use; see null.model parameter in ses.mntd function of picante package for available options.

abundance.weighted

default FALSE; Should mean nearest taxon distances for each species be weighted by species abundance?

runs

default 999; Number of randomizations.

...

paremeters pass to ses.mntd function in picante package.

Returns

res_NTI in object, equivalent to -1 times ses.mntd.

Examples
\donttest{
# only run 50 times for the example; default 999
t1$cal_NTI(null.model = "taxa.labels", abundance.weighted = TRUE, runs = 50)
}

Method cal_Cscore()

Calculates the (normalised) mean number of checkerboard combinations (C-score) using C.score function in bipartite package.

Usage
trans_nullmodel$cal_Cscore(by_group = NULL, ...)
Arguments
by_group

default NULL; one column name or number in sample_table; calculate C-score for different groups separately.

...

paremeters pass to C.score function in bipartite package.

Returns

results directly.

Examples
\dontrun{
t1$cal_Cscore()
}

Method cal_tNST()

Calculate normalized stochasticity ratio (NST) based on the tNST function of NST package.

Usage
trans_nullmodel$cal_tNST(group, ...)
Arguments
group

a colname of sample_table; the function can select the data from sample_table to generate a one-column (n x 1) matrix and provide it to the group parameter of tNST function.

...

paremeters pass to tNST function of NST package; see the documents of tNST function for more details.

Returns

.

Examples
\dontrun{
t1$cal_tNST(group = "Group", dist.method = "bray", output.rand = TRUE, SES = TRUE)
}

Method cal_tNST_test()

Test the significance of NST difference between each pair of groups.

Usage
trans_nullmodel$cal_tNST_test(method = "nst.boot", ...)
Arguments
method

default "nst.boot"; "nst.boot" or "nst.panova"; see NST::nst.boot function or NST::nst.panova function for the details.

...

paremeters pass to NST::nst.boot when method = "nst.boot" or NST::nst.panova when method = "nst.panova"

Returns

.

Examples
\dontrun{
t1$cal_tNST_test()
}

Method clone()

The objects of this class are cloneable with this method.

Usage
trans_nullmodel$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `trans_nullmodel$new`
## ------------------------------------------------

data(dataset)
data(env_data_16S)
t1 <- trans_nullmodel$new(dataset, filter_thres = 0.0005, add_data = env_data_16S)

## ------------------------------------------------
## Method `trans_nullmodel$cal_mantel_corr`
## ------------------------------------------------


t1$cal_mantel_corr(use_env = "pH")


## ------------------------------------------------
## Method `trans_nullmodel$plot_mantel_corr`
## ------------------------------------------------


t1$plot_mantel_corr()


## ------------------------------------------------
## Method `trans_nullmodel$cal_betampd`
## ------------------------------------------------


t1$cal_betampd(abundance.weighted = TRUE)


## ------------------------------------------------
## Method `trans_nullmodel$cal_betamntd`
## ------------------------------------------------


t1$cal_betamntd(abundance.weighted = TRUE)


## ------------------------------------------------
## Method `trans_nullmodel$cal_ses_betampd`
## ------------------------------------------------


# only run 50 times for the example; default 1000
t1$cal_ses_betampd(runs = 50, abundance.weighted = TRUE)


## ------------------------------------------------
## Method `trans_nullmodel$cal_ses_betamntd`
## ------------------------------------------------


# only run 50 times for the example; default 1000
t1$cal_ses_betamntd(runs = 50, abundance.weighted = TRUE, exclude.conspecifics = FALSE)


## ------------------------------------------------
## Method `trans_nullmodel$cal_rcbray`
## ------------------------------------------------


# only run 50 times for the example; default 1000
t1$cal_rcbray(runs = 50)


## ------------------------------------------------
## Method `trans_nullmodel$cal_process`
## ------------------------------------------------


t1$cal_process(use_betamntd = TRUE)


## ------------------------------------------------
## Method `trans_nullmodel$cal_NRI`
## ------------------------------------------------


# only run 50 times for the example; default 999
t1$cal_NRI(null.model = "taxa.labels", abundance.weighted = FALSE, runs = 50)


## ------------------------------------------------
## Method `trans_nullmodel$cal_NTI`
## ------------------------------------------------


# only run 50 times for the example; default 999
t1$cal_NTI(null.model = "taxa.labels", abundance.weighted = TRUE, runs = 50)


## ------------------------------------------------
## Method `trans_nullmodel$cal_Cscore`
## ------------------------------------------------

## Not run: 
t1$cal_Cscore()

## End(Not run)

## ------------------------------------------------
## Method `trans_nullmodel$cal_tNST`
## ------------------------------------------------

## Not run: 
t1$cal_tNST(group = "Group", dist.method = "bray", output.rand = TRUE, SES = TRUE)

## End(Not run)

## ------------------------------------------------
## Method `trans_nullmodel$cal_tNST_test`
## ------------------------------------------------

## Not run: 
t1$cal_tNST_test()

## End(Not run)

microeco documentation built on June 22, 2022, 9:07 a.m.