\docType{data}
\name{data-soilrep}
\alias{data-soilrep}
\alias{soilrep}
\title{(Data) Reproducibility of soil microbiome data (2011)}
\description{
  Published in early 2011, this work compared 24 separate
  soil microbial communities under four treatment
  conditions via multiplexed/barcoded 454-pyrosequencing of
  PCR-amplified 16S rRNA gene fragments. The authors found
  differences in the composition and structure of microbial
  communities between soil treatments. As expected, the
  soil microbial communities were highly diverse, with a
  staggering 16,825 different OTUs (species) observed in
  the included dataset. Interestingly, this study used a
  larger number of replicates than previous studies of this
  type, for a total of 56 samples, and the putatively low
  resampling rate of species between replicated sequencing
  trials (``OTU overlap'') was a major concern by the
  authors.
}
\details{
  This dataset contains an experiment-level
  (\code{\link{phyloseq-class}}) object, which in turn
  contains the taxa-contingency table and soil-treatment
  table as \code{\link{otuTable-class}} and
  \code{\link{sampleData-class}} components, respectively.

  This data was imported from raw files supplied directly
  by the authors via personal communication for the
  purposes of including as an example in the
  \code{\link{phyloseq-package}}. As this data is sensitive
  to choices in OTU-clustering parameters, attempts to
  recreate the \code{otuTable} from the raw sequencing data
  may give slightly different results than the table
  provided here.

  abstract from research article (quoted):

  To determine the reproducibility and quantitation of the
  amplicon sequencing-based detection approach for
  analyzing microbial community structure, a total of 24
  microbial communities from a long-term global change
  experimental site were examined. Genomic DNA obtained
  from each community was used to amplify 16S rRNA genes
  with two or three barcode tags as technical replicates in
  the presence of a small quantity (0.1\% wt/wt) of genomic
  DNA from Shewanella oneidensis MR-1 as the control. The
  technical reproducibility of the amplicon
  sequencing-based detection approach is quite low, with an
  average operational taxonomic unit (OTU) overlap of
  17.2\%\code{+/-}2.3\% between two technical replicates,
  and 8.2\%\code{+/-}2.3\% among three technical
  replicates, which is most likely due to problems
  associated with random sampling processes. Such
  variations in technical replicates could have substantial
  effects on estimating beta-diversity but less on
  alpha-diversity. A high variation was also observed in
  the control across different samples (for example,
  66.7-fold for the forward primer), suggesting that the
  amplicon sequencing-based detection approach could not be
  quantitative. In addition, various strategies were
  examined to improve the comparability of amplicon
  sequencing data, such as increasing biological
  replicates, and removing singleton sequences and
  less-representative OTUs across biological replicates.
  Finally, as expected, various statistical analyses with
  preprocessed experimental data revealed clear differences
  in the composition and structure of microbial communities
  between warming and non-warming, or between clipping and
  non-clipping. Taken together, these results suggest that
  amplicon sequencing-based detection is useful in
  analyzing microbial community structure even though it is
  not reproducible and quantitative. However, great caution
  should be taken in experimental design and data
  interpretation when the amplicon sequencing-based
  detection approach is used for quantitative analysis of
  the beta-diversity of microbial communities.

  (end quote)
}
\examples{
# Load the data
data(soilrep)
################################################################################
# Alpha diversity (richness) example. Accept null hypothesis:
# No convincing difference in species richness between warmed/unwarmed soils.
################################################################################
DF <- data.frame(sampleData(soilrep), estimate_richness(soilrep) )
# Create ggplot2-boxplot comparing the different treatments.
man.col <- c(WC="red", WU="brown", UC="blue", UU="darkgreen")
p <- plot_richness_estimates(soilrep, x="Treatment", color="Treatment")
p + geom_boxplot() + scale_color_manual(values=man.col)
# The treatments do not appear to have affected the
# estimated total richness between warmed/unwarmed soil samples
t.test(x=subset(DF, warmed=="yes")[, "S.chao1"], y=subset(DF, warmed=="no")[, "S.chao1"])
################################################################################
# A beta diversity comparison.
################################################################################
# Perform non-metric multidimensional scaling, using Bray-Curtis distance
soil.NMDS <- ordinate(soilrep, "NMDS", "bray")
p <- plot_ordination(soilrep, soil.NMDS, "samples", color="Treatment")
( p <- p + geom_point(size=5, alpha=0.5) + facet_grid(warmed ~ clipped) )
}
\author{
  Jizhong Zhou, et al.
}
\references{
  Zhou, J., Wu, L., Deng, Y., Zhi, X., Jiang, Y.-H., Tu,
  Q., Xie, J., et al. Reproducibility and quantitation of
  amplicon sequencing-based detection. The ISME Journal.
  (2011) 5(8):1303-1313. \code{doi:10.1038/ismej.2011.11}

  The article can be accessed online at
  \url{http://www.nature.com/ismej/journal/v5/n8/full/ismej201111a.html}
}
\keyword{data}