mcmc.otu: Analyzes multivariate counts data using poisson-lognormal...
In MCMC.OTU: Bayesian Analysis of Multivariate Counts Data in DNA Metabarcoding and Ecology

Description Usage Arguments Details Value Author(s) References See Also Examples

Wrapper function for MCMCglmm by Jarrod Hadfield, designed for multivariate counts data such as in sequence-based analysis of microbial communities ("metabarcoding", variables = operational taxonomic units, OTUs), or in ecological applications (variables = species). The function aims to infer the changes in relative proportions of individual variables. The maximum number of variables that can be processed on a laptop computer is about 200; more memory is required for larger numbers.

1 2	mcmc.otu(fixed=NULL, random=NULL, data, y.scale="proportion", globalMainEffects="remove", vprior="uninf",...)

`fixed`	combination of fixed effects, as a text string. Do not use "*" symbol, list it fully, such as: 'factor1+factor2+factor1:factor2'.
`random`	A vector of names for variable-specific scalar random effects, such as 'c("effect1","effect2")'.
`data`	output of the otuStack() function
`y.scale`	By default, the modeled abundances will be expressed relative to the total counts in the sample, effectively corresponding to proportions of total. Specify 'y.scale="absolute"' to express the results as absolute abundances.
`globalMainEffects`	By default, the model will assume that the samples can vary systematically in abundance among factor combinations (i.e., there is an effect of a factor combination applicable to all variables) and remove these effects; this is analogous to normalizing the samples to total counts. Specify 'globalMainEffects="keep"' to switch this off.
`vprior`	Prior for variance of user-specified random effects. By default an inverse Wishart prior with assumed variance (V) at 1 and the degree of belief parameter (nu) at 0. With 'prior="iw"' and 'prior="iw01"' nu is the number of OTUs minus 0.998, resulting in a weakly informative prior that is commonly used in this type of inference. vprior="iw" will assume large prior variance (1), vprior="iw01" will assume small prior variance (0.1). If the model has trouble converging, specify vptior="iw".
`...`	other options for MCMCglmm function, such as nitt (number of iterations), thin (tinning interval), and burnin (number of initial iterations to disregard). For a more precise inference, specify 'nitt=50000, thin=25, burnin=5000'. See MCMCglmm documentation for more details.

This function constructs priors and runs an MCMC chain to fit a Poisson-lognormal generalized linear mixed model to the multivariate counts data.

The fixed effects for the model by default include a variable-specific intercept, global (non-variable-specific) main effects of fixed factors, and variable-specific effect for each of the listed fixed factors. With globalMainEffects="keep" the model will not include the global main effects, resulting in them being absorbed into the variable-specific effects.

The user-specified random effects are all assumed to be variable-specific with no covariances.

The model includes one universal random factor: the scalar random effect of sample, which accounts for the unequal counting effort among samples.

Residual variances are assumed to be variable-specific with no covariances, with weakly informative inverse Wishart prior with variance=1 and nu=(number of variables)-0.998.

The priors for fixed effects are diffuse gaussians with a mean at 0 and very large variances (1e+8),

An MCMCglmm object. OTUsummary() function within this package summaizes these data, calculates all variable-wise credible intervals and p-values, and plots the results either as line-point-whiskers graph or a bar-whiskers graph using ggplot2 functions.

OTUsummary() only works for experiments with a single multilevel factor or two fully crossed multilevel factors.

For more useful operations on MCMCglmm objects, such as posterior.mode(), HPDinterval(), and plot(), see documentation for MCMCglmm package.

Mikhail V. Matz, University of Texas at Austin <matz@utexas.edu>

Elizabeth A. Green, Sarah W. Davies, Mikhail V. Matz, Monica Medina Next-generation sequencing reveals cryptic Symbiodinium diversity within Orbicella faveolata and Orbicella franksi at the Flower Garden Banks, Gulf of Mexico. PeerJ 2014 https://peerj.com/preprints/246/

OTUsummary(),MCMCglmm()

	
# Symbiodinium sp diversity in two coral species at two reefs (banks)
data(green.data)

# removing outliers
goods=purgeOutliers(
	data=green.data,
	count.columns=c(4:length(green.data[1,])),
	zero.cut=0.25 # remove this line for real analysis
)

# stacking the data table
gs=otuStack(
	data=goods,
	count.columns=c(4:length(goods[1,])),
	condition.columns=c(1:3)
	)

# fitting the model
mm=mcmc.otu(
	fixed="bank+species+bank:species",
	data=gs,
	nitt=3000,burnin=2000 # remove this line for real analysis!
	)

# selecting the OTUs that were modeled reliably
acpass=otuByAutocorr(mm,gs)

# calculating effect sizes and p-values:
ss=OTUsummary(mm,gs,summ.plot=FALSE)

# correcting for mutliple comparisons (FDR)
ss=padjustOTU(ss)

# getting significatly changing OTUs (FDR<0.05)
sigs=signifOTU(ss)

# plotting them
ss2=OTUsummary(mm,gs,otus=sigs)

# bar-whiskers graph of relative changes:
# ssr=OTUsummary(mm,gs,otus=signifOTU(ss),relative=TRUE)

# displaying effect sizes and p-values for significant OTUs
ss$otuWise[sigs]