RC.bin.cm: Calculate modified Roup-Crick index based on Bray-Curtis...

In iCAMP: Infer Community Assembly Mechanisms by Phylogenetic-Bin-Based Null Model Analysis

Calculate modified Roup-Crick index based on Bray-Curtis similarity for each phylogenetic bin under multiple metacommunities

Description

Calculate modified Roup-Crick index based on Bray-Curtis similarity (RC.Bray) for each phylogenetic bin. The null model algorithm will randomize the whole community data matrix of all bins. This function can deal with local communities under different metacommunities (regional pools).

Usage

RC.bin.cm(com, sp.bin, rand = 1000, na.zero = TRUE,
          meta.group = NULL, meta.frequency = NULL,
          meta.ab = NULL, nworker = 4, memory.G = 50,
          big.method = c("loop", "no"), weighted = TRUE,
          unit.sum = NULL, sig.index = c("RC", "Confidence", "SES"),
          detail.null = FALSE, output.bray = FALSE,
          taxo.metric = "bray", transform.method = NULL,
          logbase = 2, dirichlet = FALSE)

Arguments

com	community data matrix. rownames are sample names. colnames are species names.
sp.bin	one-column matrix, rownames are taxa IDs (i.e. OTU IDs), the only column shows the bin ID of each taxon. Bin IDs are integers.
rand	integer, randomization times. default is 1000.
na.zero	logic. If community data marix has any zero-sum row (sample), Bray-Curtis index will be NA. Somtimes, this kind of NA need be set as zero to avoid some format problem in following calculation. Default is TRUE.
meta.group	matrix or data.frame, a one-column (n x 1) matrix indicating which metacommunity each sample belongs to. Rownames are sample IDs. The first column is metacommunity names. Such that different samples can belong to different metacommunities. If input a n x m matrix, only the first column is used. NULL means all samples belong to the same metacommunity. Default is NULL, means all samples from the same metacommunity.
meta.frequency	matrix or data.frame, each column represents a taxon, each row represents a metacommunity (regional pool), to define the occurrence frequency of each taxon in each metacommunity. The rownames indicate metacommunity names, which should be the same as the metacommunity names in meta.group. Default setting is NULL, means to calculate meta.frequency as occurrence frequency of each taxon in comm across the samples within each metacommunity defined by meta.group.
meta.ab	matrix or data.frame, each column represents a taxon, each row represents a metacommunity (regional pool), to define the aubndance (or relative abundance) of each taxon in each metacommunity. The rownames indicate metacommunity names, which should be the same as the metacommunity names in meta.group. Default setting is NULL, means to calculate meta.ab as average relative abundance of each taxon in comm across the samples within each metacommunity defined by meta.group.
nworker	for parallel computing. Either a character vector of host names on which to run the worker copies of R, or a positive integer (in which case that number of copies is run on localhost). default is 4, means 4 threads will be run.
memory.G	numeric, to set the memory size as you need, so that calculation of big data will not be limited by physical memory. unit is Gb. default is 50Gb.
big.method	character, the method to handle big data. loop, randomization once after another; no, use parallel computing.
weighted	Logic, consider abundances or not (just presence/absence). default is TRUE.
unit.sum	If unit.sum is set as a number or a numeric vector, the taxa abundances will be divided by unit.sum to calculate the relative abundances, and the Bray-Cuits index in each bin will become manhattan index divided by 2. usually, unit.sum can be set as the sequencing depth in each sample. Default setting is NULL, means not to do this special transformation.
sig.index	character, the index for null model significance test. RC, modified Raup-Crick index (RC) based on taxonomic dissimilarity (default is Bray-Curtis, BC), i.e. count the number of null BC lower than observed BC plus a half of the number of null BC equal to observed BC, to get alpha, then calculate RCbray as (2 x alpha - 1). SES, standard effect size; Confidence, percentage of null values less extreme than the observed value, i.e. non-parametric one-side confidence level. default is RC. If input a vector, only the first element will be used.
detail.null	logic, if TRUE, the output will include all the null values. Default is FALSE.
output.bray	logic, if TRUE, the output will include observed taxonomic dissimilarity (default is Bray-Curtis).
taxo.metric	taxonomic beta diversity index, the same as 'method' in the function 'vegdist' in package 'vegan', including "manhattan", "euclidean", "canberra", "clark", "bray", "kulczynski", "jaccard", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao" or "mahalanobis". If taxo.metric='bray' and transform.method=NULL, RC will be calculated based on Bray-Curtis dissimilarity as recommended in original iCAMP; otherwise, unit.sum setting will be ignored.
transform.method	character or a defined function, to specify how to transform community matrix before calculating dissimilarity. if it is a characher, it should be a method name as in the function 'decostand' in package 'vegan', including 'total','max','freq','normalize','range','standardize','pa','chi.square','cmdscale','hellinger','log'.
logbase	numeric, the logarithm base used when transform.method='log'.
dirichlet	Logic. If TRUE, the taxonomic null model will use Dirichlet distribution to generate relative abundances in randomized community matrix. If the input community matrix has all row sums no more than 1, the function will automatically set dirichlet=TRUE. default is FALSE.

Details

This function is particularly designed for samples from different metacommunities. The null model will randomize the commuity matrix under different metacommunities, separately (and independently). All other details are the same as the function RC.bin.bigc.

Value

Output is a list.

index	list, each element is a square matrix of RC (or SES or Confidence based on Bray-Curtis) values of a bin. The elements (bins) are in the same order as in the input pdid.bin.
sig.index	character, indicates the index for null model significance test, RC, Confidence, or SES.
BC.obs	Output only if output.bray is TRUE. A list, each element is a square matrix of observed taxonomic dissimilarity (default is Bray-Curtis) index values of a bin. The elements (bins) are in the same order as in the input pdid.bin.
rand	Output only if detail.null is TRUE. A list, each element is a matrix with null values of Bray-Curtis index for each turnover of a bin. The elements (bins) are in the same order as in the input pdid.bin.

Note

Version 1: 2021.8.4

Author(s)

Daliang Ning

References

Ning, D., Yuan, M., Wu, L., Zhang, Y., Guo, X., Zhou, X. et al. (2020). A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nature Communications, 11, 4717.

Chase, J.M., Kraft, N.J.B., Smith, K.G., Vellend, M. & Inouye, B.D. (2011). Using null models to disentangle variation in community dissimilarity from variation in alpha-diversity. Ecosphere, 2, 1-11.

Stegen, J.C., Lin, X., Fredrickson, J.K., Chen, X., Kennedy, D.W., Murray, C.J. et al. (2013). Quantifying community assembly processes and identifying features that impose them. ISME J, 7, 2069.

Kraft, N.J.B., Comita, L.S., Chase, J.M., Sanders, N.J., Swenson, N.G., Crist, T.O. et al. (2011). Disentangling the drivers of beta diversity along latitudinal and elevational gradients. Science, 333, 1755-1758.

See Also

RC.bin.bigc,RC.cm

Examples

data("example.data")
comm=example.data$comm
sp.bin=example.data$sp.bin

# in this example, 10 samples from one metacommunity,
# the other 10 samples from another metacommunity.
meta.group=data.frame(meta.com=c(rep("meta1",10),rep("meta2",10)))
rownames(meta.group)=rownames(comm)

rand.time=20 # usually use 1000 for real data.
nworker=2 # parallel computing thread number
RCbin=RC.bin.cm(com=comm, meta.group=meta.group,
                sp.bin=sp.bin, rand=rand.time,
                nworker=nworker, weighted=TRUE,
                sig.index="RC")

iCAMP documentation built on June 1, 2022, 9:08 a.m.