\name{EvoWeaver-PSPreds}
\alias{EvoWeaver-PSPreds}
\alias{MirrorTree.EvoWeaver}
\alias{ContextTree.EvoWeaver}
\alias{TreeDistance.EvoWeaver}
\docType{data}
\title{
Phylogenetic Structure Predictions for EvoWeaver
}
\description{
\code{EvoWeaver} incorporates four classes of prediction, each with multiple 
methods and algorithms. Phylogenetic Structure (PS) methods examine conservation 
of overall evolutionary rates within orthology groups using distance matrices
constructed from each gene tree.

\code{predict.EvoWeaver} currently supports three PS methods:
\itemize{
  \item \code{'MirrorTree'}
  \item \code{'ContextTree'}
  \item \code{'TreeDistance'}
}
}

\details{
All distance matrix methods require a \code{EvoWeaver} object initialized
with \code{dendrogram} objects. See \code{\link{EvoWeaver}} for more information on input data types.

The \code{MirrorTree} method was introduced by Pazos et al. (2001). This method
builds distance matrices using a nucleotide substitution model, and then
calculates coevolution between gene families using the Pearson correlation 
coefficient of the upper triangle of the two corresponding matrices. 

Experimental analysis has shown data in the upper triangle is heavily
redundant and rapidly overwhelms available system memory. Previous work
has incorporated dimensionality reduction such as SVD to reduce the dimensionality
of the data, but this prevents parallelization of the data and doesn't solve memory
issues (since SVD takes as input the entire matrix with columns corresponding to upper triangle values). \code{EvoWeaver} instead uses a seeded random projection
following Achlioptas (2001) to reduce the dimensionality of the data in a 
reproducible and parallel-compatible way. We also utilize Spearman's \eqn{\rho},
which outperforms Pearson's \eqn{r} following dimensionality reduction.

Subsequent work by Pazos et al. (2005) and Sato et al. (2005, 2006) found
multiple ways to improve predictions from the initial \code{MirrorTree} method.
These methods incorporate additional phylogenetic context, and are thus
called \code{ContextTree} methods. These improvements include correcting
for overall evolutionary rate using a species tree and/or using projection vectors.
The built-in \code{ContextTree} method implements a species tree correction, and weights the resulting score by the normalized Hamming distance of the presence/absence profiles. This can correct for gene trees with low overlap that achieve spuriously high scores via random projection. Additional correction measures are implemented in the \code{MTCorrection} argument.

The \code{TreeDistance} method uses phylogenetic tree distance to quantify differences between gene trees. This method implements a number of metrics and groups them together to improve overall runtime. The default tree distance method is normalized Robinson-Foulds distance due to its lower computational complexity. Other methods can be specified using the \code{TreeMethods} argument, which expects a character vector containing one or more of the following:
\itemize{
\item \code{"CI"}: \link[=CIDist]{Clustering Information Distance}
\item \code{"RF"}: \link[=RFDist]{Robinson-Foulds Distance}
\item \code{"JRF"}: \link[=JRFDist]{Jaccard-Robinson-Foulds Distance}
\item \code{"Nye"}: \link[=JRFDist]{Nye Similarity}
\item \code{"KF"}: \link[=KFDist]{Kuhner-Felsenstein Distance}
\item \code{"all"}: All of the above methods
}

See the links above for more information and references. All of these metrics are accessible using the \code{\link{PhyloDistance}} method. Method \code{"JRF"} defaults to a \code{k}
value of 4, but this can be specified further if necessary using the \code{JRFk} input parameter. Higher values of \code{k} approach the value of Robinson-Foulds 
distance, but these have a negligible impact on performance so use of the default parameter is encouraged for simplicity. Multiple metrics can be specified.
}

\value{
  None.
}

\author{
Aidan Lakshman \email{ahl27@pitt.edu}
}

\references{
Achlioptas, Dimitris. \emph{Database-friendly random projections.} Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, 2001. 
p. 274-281.

Pazos, F. and A. Valencia, \emph{Similarity of phylogenetic trees as indicator of protein–protein interaction.} Protein Engineering, Design and Selection, 2001. \bold{14}(9): p. 609-614.

Pazos, F., et al., \emph{Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome.} J Mol Biol, 2005. \bold{352}(4): p. 1002-15.

Sato, T., et al., \emph{The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships.} Bioinformatics, 2005. \bold{21}(17): p. 3482-9.

Sato, T., et al., \emph{Partial correlation coefficient between distance matrices as a new indicator of protein-protein interactions.} Bioinformatics, 2006. \bold{22}(20): p. 2488-92.
}

\seealso{

\code{\link{EvoWeaver}}

\code{\link{predict.EvoWeaver}}

\link[=EvoWeaver-PPPreds]{EvoWeaver Phylogenetic Profiling Predictors}

\link[=EvoWeaver-GOPreds]{EvoWeaver Gene Organization Predictors}

\link[=EvoWeaver-SLPreds]{EvoWeaver Sequence-Level Predictors}

\code{\link{PhyloDistance}}
}