version 1.1.0

DESCRIPTION

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+Package: propr
+Title: Calculating Proportionality Between Vectors of Compositional
+        Data
+Version: 1.1.0
+URL: http://github.com/tpq/propr
+BugReports: http://github.com/tpq/propr/issues
+Authors@R: c(
+    person("Thomas", "Quinn", email = "contacttomquinn@gmail.com", role = c("aut", "cre")),
+    person("David", "Lovell", email = "david.lovell@qut.edu.au", role = "aut")
+    )
+Description: The bioinformatic evaluation of gene co-expression often begins with
+    correlation-based analyses. However, this approach lacks statistical validity
+    when applied to relative data, including those biological count data produced by
+    microarray assays or high-throughput RNA-sequencing. As an alternative, Lovell
+    et al propose a proportionality metric, phi, derived from compositional data
+    analysis, a branch of math dealing specifically with relative data. In a
+    subsequent publication, Erb and Nicodemus expounded these efforts by elaborating
+    on another proportionality metric, rho. This package introduces a programmatic
+    framework for the calculation of feature dependence using proportionality and
+    other compositional data methods discussed in the cited publications.
+License: GPL-2
+LazyData: true
+VignetteBuilder: knitr
+RoxygenNote: 5.0.1
+Depends: R (>= 3.2.2)
+Imports: methods
+Suggests: compositions, dendextend, ggplot2, ggthemes, knitr,
+        rmarkdown, testthat
+NeedsCompilation: no
+Packaged: 2016-07-06 00:08:59 UTC; thom
+Author: Thomas Quinn [aut, cre],
+  David Lovell [aut]
+Maintainer: Thomas Quinn <contacttomquinn@gmail.com>
+Repository: CRAN
+Date/Publication: 2016-07-06 09:44:00

MD5

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+455c771cf8481b26401d90907353a3aa *DESCRIPTION
+156ec54e98b9e35ed47a671d223edf1b *NAMESPACE
+d42464af95b40021761560662d6f709d *NEWS.md
+202017784e399813d15f595fff4440e4 *R/propr-backend.R
+a50b04d7046c0b703e4e7098de5f3ddf *R/propr-classes.R
+9c4cd2f34759be4bea23c8eed7d7bb7f *R/propr-methods.R
+e71cf145895d640e635811964126b17c *R/propr-perb.R
+628ec584b94b4b595b5f3b7c9aae07ba *R/propr-phit.R
+eb65f255609f369fa209912ea0065afb *README.md
+79086a6786543eda37d50ee59b2d9da5 *build/vignette.rds
+7e8b32c5ecd2d10e82aed71f928be93a *inst/doc/propr-vignette.R
+d4bfa5c80c697c188330a0c205b86d13 *inst/doc/propr-vignette.Rmd
+2cdfc1f722d89face3c159481581484a *inst/doc/propr-vignette.html
+3a1924d0443f34ddfc7e8a051dea4b37 *man/perb.Rd
+e82dfb7b8d74048aa755b946858288ff *man/phit.Rd
+c1f20b8969faf5bbd5a0399e54ab615d *man/propr-class.Rd
+d5ccc56298a3b1d5e04968cfc4d8d74e *man/propr.Rd
+0fe59d89c9611cf4301cbc8f43cb2fa3 *man/proprALR.Rd
+b3c307949b6b75260172ed7ef0997fdd *man/proprCLR.Rd
+f3ca7b8b03b6b44ee558ae5c00010048 *man/proprPairs.Rd
+2287bdb852156bbe3d98830c561dc115 *man/proprPerb.Rd
+18fb6daf85bb1e408a53dde4b679a392 *man/proprPhit.Rd
+6082a9b422e214509bfac10bd6f93a2c *man/proprSym.Rd
+d2e912419a153a83fb9753300bfd7d9d *man/proprTri.Rd
+d3b180f9c085eb5ee6cb51db23dbe723 *man/proprVLR.Rd
+38a416384180a93d31020bdffb37ccee *tests/testthat.R
+fc05d9b4d260a59e5ee9868520801bd6 *tests/testthat/test-logratio.R
+18bea904004f38c52e8236832c08f21a *tests/testthat/test-perb.R
+fb995286047319340ea951b7fb50b49a *tests/testthat/test-phit.R
+23378d943d79a9bf3fb14ed100337e57 *tests/testthat/test-vlr.R
+d4bfa5c80c697c188330a0c205b86d13 *vignettes/propr-vignette.Rmd

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(dendrogram)
+export(perb)
+export(phit)
+exportClasses(propr)
+exportMethods("$")
+exportMethods("[")
+exportMethods(image)
+exportMethods(plot)
+exportMethods(show)
+exportMethods(subset)
+importFrom(grDevices,rainbow)
+importFrom(methods,new)
+importFrom(methods,show)
+importFrom(stats,as.dendrogram)
+importFrom(stats,as.dist)
+importFrom(stats,ecdf)
+importFrom(stats,hclust)
+importFrom(stats,order.dendrogram)
+importFrom(stats,p.adjust)

NEWS.md

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+## propr 1.1.0
+---------------------
+* New orientation expected for input data
+  * Updated back-end and vignette accordingly
+  * Removed redundant transpositions
+* Fixed rare subsetting errors
+* Tweaked plot methods
+
+## propr 1.0.0
+---------------------
+* Introduced `phit` function
+  * Implements Lovell's \phi proportionality metric
+  * Returns object of class `propr`
+* Introduced `perb` function
+  * Implements Erb's \rho proportionality metric
+  * Returns object of class `propr`
+* Introduced `propr` Class
+  * `show` method
+    * Subsets `propr` based on `@pairs` slot
+  * `subset` method
+    * Subsets `propr` based on `@matrix` slot
+  * `plot` method
+    * Plots pairwise *lr proportionality
+  * `dendrogram` method
+    * Plots clusters of *lr-transformed data
+  * `image` method
+    * Plots heatmap of *lr-transformed data

R/propr-backend.R

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+#' Calculate proportionality metric phi (Lovell 2015).
+#'
+#' Provided for backend use.
+#'
+#' @inheritParams phit
+#' @return Returns proportionality matrix.
+proprPhit <- function(counts, symmetrize = TRUE){
+
+  # Replace zeroes with next smallest number
+  counts[counts == 0] <- unique(sort(as.matrix(counts)))[2]
+
+  # Calculate the variance of the log-ratio ("variation array")
+  counts.vlr <- proprVLR(counts)
+  colnames(counts.vlr) <- colnames(counts)
+  rownames(counts.vlr) <- colnames(counts)
+
+  # Calculate feature variance across clr transformed treatments
+  counts.clr <- proprCLR(counts)
+  counts.clr.var <- apply(counts.clr, 2, stats::var)
+
+  # Sweep out feature clr variance from the variation array
+  phi <- sweep(counts.vlr, 2, counts.clr.var, FUN = "/")
+
+  # Symmetrize matrix if symmetrize = TRUE
+  if(symmetrize) phi <- proprSym(phi)
+
+  return(phi)
+}
+
+#' Calculate proportionality metric rho (Erb 2016).
+#'
+#' Provided for backend use.
+#'
+#' @inheritParams phit
+#' @inheritParams perb
+#' @return Returns proportionality matrix.
+proprPerb <- function(counts, ivar = 0){
+
+  # Replace zeroes with next smallest number
+  counts[counts == 0] <- unique(sort(as.matrix(counts)))[2]
+
+  # Calculate the variance of the log-ratio ("variation array")
+  counts.vlr <- proprVLR(counts)
+  colnames(counts.vlr) <- colnames(counts)
+  rownames(counts.vlr) <- colnames(counts)
+
+  if(ivar != 0){
+
+    # Calculate feature variance across alr transformed treatments
+    counts.vlr <- counts.vlr[-ivar, -ivar] # returns one less dimension
+    counts.alr <- proprALR(counts, ivar = ivar) # returns one less dimension
+    counts.var <- apply(counts.alr, 2, stats::var)
+
+  }else{
+
+    # Calculate feature variance across clr transformed treatments
+    counts.clr <- proprCLR(counts)
+    counts.var <- apply(counts.clr, 2, stats::var)
+  }
+
+  # Divide variation array by sum of feature variances
+  for(i in 1:ncol(counts.vlr)){
+    for(j in 1:nrow(counts.vlr)){
+      counts.vlr[i, j] <- counts.vlr[i, j] / (counts.var[i] + counts.var[j])
+    }
+  }
+
+  # Calculate: p = 1 - (var(x - y))/(var(x) + var(y))
+  rho <- 1 - counts.vlr
+
+  return(rho)
+}
+
+#' Calculates the variance of the log of the ratios.
+#'
+#' Provided for backend use.
+#'
+#' @param X A data.frame or matrix. A "count matrix" with subjects as rows and features as columns.
+#' @param check A logical. If TRUE, function first checks for negative and NA values.
+#' @return Returns a matrix containing the variance of the log of the ratios.
+proprVLR <- function(X, check = FALSE){
+
+  if(check){
+
+    if(any(X < 0))    stop("negative values found")
+    if(any(is.na(X))) stop("NA values found")
+  }
+
+  logX <- log(X)
+  Cov    <- stats::var(logX)  ## Note the avoidance of compositions::var
+  D      <- ncol(logX)
+  VarCol <- matrix(rep(diag(Cov), D), ncol = D)
+  return(-2 * Cov + VarCol + t(VarCol))
+}
+
+#' Calculates the centered log-ratio transformation.
+#'
+#' Provided for backend use.
+#'
+#' @inheritParams proprVLR
+#' @return A matrix. Returns the centered log-ratio transformation of \code{X}.
+proprCLR <- function(X, check = FALSE){
+
+  if(check){
+
+    if(any(X < 0))    stop("negative values found")
+    if(any(is.na(X))) stop("NA values found")
+  }
+
+  logX <- log(X)
+  return(sweep(logX, 1, rowMeans(logX), "-")) # subtract out the means
+}
+
+#' Calculates the additive log-ratio transformation.
+#'
+#' Provided for backend use.
+#'
+#' @param ivar A numeric scalar. Specificies feature to use as reference for additive log-ratio transformation.
+#' @inheritParams proprVLR
+#' @return A matrix. Returns the additive log-ratio transformation of \code{X}.
+proprALR <- function(X, ivar, check = FALSE){
+
+  if(check){
+
+    if(any(X < 0))    stop("negative values found")
+    if(any(is.na(X))) stop("NA values found")
+  }
+
+  logX <- log(X[, -ivar])
+  return(sweep(logX, 1, log(X[, ivar]), "-")) # subtract out the ivar
+}
+
+#' Recasts proportionality matrix as a table of feature pairs.
+#'
+#' Provided for backend use.
+#'
+#' @param prop A data.frame or matrix. A proportionality matrix.
+#' @return A data.frame. Returns a table of feature pairs.
+proprPairs <- function(prop){
+
+  if(identical(dim(prop), as.integer(c(1, 1)))){
+
+    return(data.frame())
+  }
+
+  index.i <- vector("numeric", length = (nrow(prop) - 1)*nrow(prop)/2)
+  index.j <- vector("numeric", length = (nrow(prop) - 1)*nrow(prop)/2)
+  index.prop <- vector("numeric", length = (nrow(prop) - 1)*nrow(prop)/2)
+  counter <- 1
+
+  for(j in 2:nrow(prop)){
+
+    for(i in 1:(j-1)){
+
+      index.i[counter] <- i
+      index.j[counter] <- j
+      index.prop[counter] <- prop[j, i]
+      counter <- counter + 1
+    }
+  }
+
+  result <- data.frame("feature1" = rownames(prop)[index.i],
+                       "feature2" = rownames(prop)[index.j],
+                       "prop" = index.prop,
+                       stringsAsFactors = FALSE)
+
+  final <- result[order(result$prop), ]
+  rownames(final) <- 1:nrow(final)
+
+  return(final)
+}
+
+#' Retrieve the lower left triangle of a proportionality matrix.
+#'
+#' Provided for backend use.
+#'
+#' @inheritParams proprPairs
+#' @return A vector. Returns the lower left triangle of a proportionality matrix.
+proprTri <- function(prop){
+
+  result <- vector("numeric", length = (nrow(prop) - 1)*nrow(prop)/2)
+  counter <- 1
+
+  for(j in 2:nrow(prop)){
+
+    for(i in 1:(j-1)){
+
+      result[counter] <- prop[j, i]
+      counter <- counter + 1
+    }
+  }
+
+  return(result)
+}
+
+#' Symmetrizes a proportionality matrix.
+#'
+#' Provided for backend use.
+#'
+#' @inheritParams proprPairs
+#' @return A matrix. Returns a symmetrized proportionality matrix.
+proprSym <- function(prop){
+
+  for(j in 2:nrow(prop)){
+
+    for(i in 1:(j-1)){
+
+      prop[i, j] <- prop[j, i]
+    }
+  }
+
+  return(prop)
+}

R/propr-classes.R

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+#' The propr Package
+#'
+#' @description
+#' Welcome to the \code{propr} package!
+#'
+#' To learn more about how to calculate metrics of proportionality,
+#'  see the help file for the method definitions \code{\link{phit}}
+#'  and \code{\link{perb}}.
+#'
+#' To learn more about the resultant \code{propr} class object, see
+#'  the help file for the class definition \code{\link{propr-class}}.
+#'
+#' To learn more about compositional data analysis, and its relevance
+#'  to biological count data, see the attached vignette.
+#'
+#' To learn more about \code{propr} class methods, see below.
+#'
+#' @name propr
+NULL
+
+#' An S4 class to hold results from proportionality analysis.
+#'
+#' @slot counts A data.frame. Stores the original "count matrix" input.
+#' @slot logratio A data.frame. Stores the log-ratio transformed "count matrix".
+#' @slot matrix A matrix. Stores the proportionality matrix calculated by \code{phit} or \code{perb}.
+#' @slot pairs A data.frame. Projects the proportionality matrix pairwise.
+#'
+#' @seealso \code{\link{propr}}, \code{\link{phit}}, \code{\link{perb}}
+#'
+#' @examples
+#' randomNum <- sample(1:1000, size = 25 * 10, replace = TRUE)
+#' counts <- matrix(randomNum, nrow = 25, ncol = 10)
+#' prop <- perb(counts, ivar = 0, iter = 0)
+#' prop[1:5, ]
+#' prop$prop
+#' prop[1:5, "prop"]
+#' subset(prop, 1:5)
+#' @export
+setClass("propr",
+         slots = c(
+           counts = "data.frame",
+           logratio = "data.frame",
+           matrix = "matrix",
+           pairs = "data.frame"
+         )
+)