ivar = NA will now skip log-ratio transform

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: propr
 Title: Calculating Proportionality Between Vectors of Compositional Data
-Version: 4.2.6
+Version: 4.2.7
 URL: http://github.com/tpq/propr
 BugReports: http://github.com/tpq/propr/issues
 Authors@R: c(
@@ -22,7 +22,7 @@ Description: The bioinformatic evaluation of gene co-expression often begins wit
 License: GPL-2
 LazyData: true
 VignetteBuilder: knitr
-RoxygenNote: 6.1.1
+RoxygenNote: 7.0.2
 Encoding: UTF-8
 Depends:
     methods,

NEWS.md

@@ -1,3 +1,7 @@
+## propr 4.2.7
+---------------------
+* Allow user to skip log-ratio transformation with `ivar = NA`
+
 ## propr 4.2.6
 ---------------------
 * Fix new R version `class` bug where matrix is also an array

R/0-params.R

@@ -12,7 +12,8 @@
 #'  for additive log-ratio transformation. The argument will also
 #'  accept feature name(s) instead of the index position(s).
 #'  Set to "iqlr" to use inter-quartile log-ratio transformation.
-#'  Ignore to use centered log-ratio transformation.
+#'  Ignore to use centered log-ratio transformation. If the data
+#'  are already transformed, skip it with \code{ivar = NA}.
 #' @param select Optional. Use this to subset the final
 #'  proportionality matrix without altering the result.
 #'  Use this argument to rearrange feature order.

R/1-propr.R

@@ -128,49 +128,59 @@ propr <- function(counts, metric = c("rho", "phi", "phs", "cor", "vlr"), ivar =
                   select, symmetrize = FALSE, alpha, p = 100){
 
   # Clean "count matrix"
-  # if(any(apply(counts, 2, function(x) all(x == 0)))){
-  #   stop("Remove components with all zeros before proceeding.")}
-  if(any(counts < 0)) stop("Data may not contain negative measurements.")
-  if(any(is.na(counts))) stop("Remove NAs from 'counts' before proceeding.")
   if("data.frame" %in% class(counts)) counts <- as.matrix(counts)
   if(is.null(colnames(counts))) colnames(counts) <- as.character(1:ncol(counts))
   if(is.null(rownames(counts))) rownames(counts) <- as.character(1:nrow(counts))
-  if(missing(alpha)){ alpha <- NA
-  }else{ if(!is.na(alpha)) if(alpha == 0) alpha <- NA }
-  ct <- counts
-
-  # Replace zeros unless alpha is provided
-  if(any(as.matrix(counts) == 0) & is.na(alpha)){
-    message("Alert: Replacing 0s with next smallest value.")
-    zeros <- ct == 0
-    ct[zeros] <- min(ct[!zeros])
-  }
+  if(any(is.na(counts))) stop("Remove NAs from 'counts' before proceeding.")
 
-  # Establish reference
-  use <- ivar2index(ct, ivar)
+  if(!is.na(ivar)){ # if a log-ratio transform is performed...
 
-  # Transform counts based on reference
-  if(is.na(alpha)){
+    if(any(counts < 0)) stop("Data may not contain negative measurements.")
 
-    # Use g(x) = Mean[log(x)] to log-ratio transform data
-    message("Alert: Saving log-ratio transformed counts to @logratio.")
-    logX <- log(ct)
-    logSet <- logX[, use, drop = FALSE]
-    ref <- rowMeans(logSet)
-    lr <- sweep(logX, 1, ref, "-")
+    if(missing(alpha)){ alpha <- NA
+    }else{ if(!is.na(alpha)) if(alpha == 0) alpha <- NA }
+    ct <- counts
 
-  }else{
+    # Replace zeros unless alpha is provided
+    if(any(as.matrix(counts) == 0) & is.na(alpha)){
+      message("Alert: Replacing 0s with next smallest value.")
+      zeros <- ct == 0
+      ct[zeros] <- min(ct[!zeros])
+    }
+
+    # Establish reference
+    use <- ivar2index(ct, ivar)
+
+    # Transform counts based on reference
+    if(is.na(alpha)){
 
-    # Since y = log(x) = [x^a-1]/a, ref = Mean[log(x)] = Mean[y]
-    # Calculate log(x/ref) = log(x) - log(ref) = [x^a-1]/a - [ref^a-1]/a
-    message("Alert: Saving alpha transformed counts to @logratio.")
-    aX <- (ct^alpha - 1) / alpha
-    aSet <- aX[, use, drop = FALSE]
-    ref <- rowMeans(aSet)
-    lr <- sweep(aX, 1, ref, "-")
+      # Use g(x) = Mean[log(x)] to log-ratio transform data
+      message("Alert: Saving log-ratio transformed counts to @logratio.")
+      logX <- log(ct)
+      logSet <- logX[, use, drop = FALSE]
+      ref <- rowMeans(logSet)
+      lr <- sweep(logX, 1, ref, "-")
+
+    }else{
+
+      # Since y = log(x) = [x^a-1]/a, ref = Mean[log(x)] = Mean[y]
+      # Calculate log(x/ref) = log(x) - log(ref) = [x^a-1]/a - [ref^a-1]/a
+      message("Alert: Saving alpha transformed counts to @logratio.")
+      aX <- (ct^alpha - 1) / alpha
+      aSet <- aX[, use, drop = FALSE]
+      ref <- rowMeans(aSet)
+      lr <- sweep(aX, 1, ref, "-")
+    }
+
+  }else{ # if skipping the log-ratio transform...
+
+    message("Alert: Skipping the log-ratio transformation.")
+    alpha <- NA
+    ct <- counts
+    lr <- ct
   }
 
-  # Optionally apply select for performance gain
+  # Optionally reduce the number of features used in matrix algebra
   if(!missing(select)){
 
     message("Alert: Using 'select' disables permutation testing.")
@@ -222,7 +232,8 @@ propr <- function(counts, metric = c("rho", "phi", "phs", "cor", "vlr"), ivar =
   # Set up @permutes
   result@permutes <- list(NULL)
   if(p > 0){
-    # Sample compositions so that the clr does not change
+
+    # Shuffle row-wise so that per-sample CLR does not change
     message("Alert: Fixing permutations to active random seed.")
     permutes <- vector("list", p)
     for(ins in 1:length(permutes)) permutes[[ins]] <- t(apply(ct, 1, sample))
@@ -242,7 +253,7 @@ propr <- function(counts, metric = c("rho", "phi", "phs", "cor", "vlr"), ivar =
     )
 
   # Initialize @results -- Tally frequency of 0 counts
-  if(any(as.matrix(counts) == 0)){
+  if(any(as.matrix(counts) == 0) & !is.na(ivar)){
     message("Alert: Tabulating the presence of 0 counts.")
     result@results$Zeros <- ctzRcpp(as.matrix(counts)) # count 0s
   }

R/6-propdViz.R

@@ -49,7 +49,6 @@ pals <- function(object, k){
 #'  where the \code{@@matrix} slot contains \eqn{1 - \theta}.
 #'  Allows the user to interrogate theta using any
 #'  visualization built for \code{propr} objects.
-#' @rdname propd
 #' @export
 propd2propr <- function(object, ivar){
 

tests/testthat/test-multiomic.R

@@ -0,0 +1,32 @@
+library(propr)
+data(iris)
+met.rel <- iris[,1:2]
+mic.rel <- iris[,3:4]
+
+# Analyze multi-omics via back-end
+clr <- function(x) sweep(log(x), 1, rowMeans(log(x)), "-")
+REL <- cbind(clr(met.rel), clr(mic.rel))
+pr.r <- propr:::lr2rho(as.matrix(REL))
+colnames(pr.r) <- colnames(REL)
+rownames(pr.r) <- colnames(REL)
+
+# Analyze multi-omics with wrapper
+ivarNA <- propr(REL, ivar = NA)
+
+test_that("ivar = NA works as expected", {
+
+  expect_equal(
+    NA,
+    ivarNA@ivar
+  )
+
+  expect_equal(
+    pr.r,
+    ivarNA@matrix
+  )
+
+  expect_equal(
+    ivarNA@counts,
+    ivarNA@logratio
+  )
+})