New package version!

DESCRIPTION

@@ -1,15 +1,15 @@
 Package: RCon3D
 Title: Analysis of Confocal Images of Microbial Biofilms
-Version: 0.0.0.9000
+Version: 1.0.0.9000
 Authors@R: person("Jakob", "Russel", email = "russel2620@gmail.com", role = c("aut", "cre"))
-Description: Various 3D analyses of confocal images. E.g. Quantification, co-aggregation and identifying aggregates.
+Description: Various 3D analyses of confocal images. E.g. Quantification, co-aggregation and identifying 3D aggregates.
 Depends: R (>= 3.2.5)
 Imports:
    foreach,
    doParallel,
    mmand,
+   tiff
 Suggests:
-   tiff,
    rootSolve
 License: MIT
 Encoding: UTF-8

NAMESPACE

@@ -1,19 +1,19 @@
 # Generated by roxygen2: do not edit by hand
 
-export(Agg)
-export(CreateNulls)
-export(CreateRandom)
-export(CrossCor)
-export(CrossCor.default)
-export(CrossRatio)
-export(CrossRatio.default)
-export(ELayers)
-export(LayerSplit)
-export(LayerStd)
-export(Quantify)
+export(clumps)
+export(co_agg)
+export(co_agg.default)
+export(create_nulls)
+export(create_random)
+export(cross_ratio)
+export(cross_ratio.default)
+export(extract_layers)
 export(findIMG)
+export(layer_split)
+export(layer_stand)
 export(loadIMG)
-export(tiffToArray)
+export(quant)
+export(tiff_to_array)
 import(doParallel)
 import(foreach)
 import(mmand)

R/Agg.R → R/clumps.R

@@ -1,95 +1,95 @@
-#' Find 3D aggregates
-#'
-#' Function to group adjacent pixels in aggregates
-#' @param imgs The paths of array files; i.e. output from loadIMG or findIMG functions.
-#' @param channel Name of the channel to find aggregates in. Should be in the names of the array files
-#' @param kern.neighbour Numeric vector indicating range of neighbouring pixels to aggregate in the x,y,z directions. Has to be odd intergers. c(1,1,1) means no aggregating.
-#' @param kern.smooth Optional. Numeric vector indicating range of median smoothing in the x,y,z directions. Has to be odd intergers. c(1,1,1) means no smoothing.
-#' @param layers Optional. Should the function only look in a subset of layers. A list with lists of layers to use for each image. Can also be the output from ELayers 
-#' @param pwidth Optional. Width of pixels in microns to calculate aggregate size in microns instead of pixels
-#' @param zstep Optional. z-step in microns to calculate aggregate size in microns instead of pixels
-#' @param naming Optional. Add metadata to the output dataframe by looking through names of array files. Should be a list of character vectors, each list element will be added as a variable. Example: naming=list(Time=c("T0","T1","T2")). The function inserts a variable called Time, and then looks through the names of the array files and inserts characters mathcing either T0, T1 or T2
-#' @keywords array image aggregate
-#' @return A list with two parts. First part is a dataframe with ID and size of aggregates and name of image, second part is a list of the arrays in which pixels are NA if empty or given a number indicating the aggregate ID
-#' @import mmand
-#' @export
-
-Agg <- function(imgs,channel,kern.neighbour=c(3,3,3),kern.smooth=NULL,layers=NULL,pwidth=NULL,zstep=NULL,naming=NULL) {
-
-  # Load image
-  ch_files <- imgs[grep(channel, imgs)]
-
-  # Lists for results
-  arrays <- list()
-  results <- list()
-
-  # For each replica
-  for(k in 1:length(ch_files)) {
-
-    message(paste("Running replica",k))
-
-    # Load
-    ch_t <- readRDS(ch_files[k])
-    side <- dim(ch_t)[1]
-    h <- dim(ch_t)[3]
-
-    # Subset layers
-    if(!is.null(layers[[k]])){
-      ch_t <- ch_t[,,layers[[k]]]
-    }
-
-    # Extend parameter
-    ep <- max(c(kern.neighbour,kern.smooth))
-
-    # Extend
-    ch_new <- array(NA, dim=c(side+(2*ep), side+(2*ep), h+(2*ep)))
-    ch_new[(ep+1):(side+ep),(ep+1):(side+ep),(ep+1):(h+ep)] <- ch_t 
-
-    # Smooth
-    if(!is.null(kern.smooth)) {
-      if(kern.smooth[1] %% 1 == 0 & kern.smooth[1] %% 2 != 0 &
-         kern.smooth[2] %% 1 == 0 & kern.smooth[2] %% 2 != 0 &
-         kern.smooth[3] %% 1 == 0 & kern.smooth[3] %% 2 != 0) {
-        kern.s <- kernelArray(array(1,dim=kern.smooth))
-        ch_new <- medianFilter(ch_new,kern.s)
-        ch_new[ch_new > 0] <- 1 } else stop("Kernel smooth has to be odd integers in all directions")
-    }
-
-    # Find aggregates
-    kern.n <- kernelArray(array(1,dim=kern.neighbour))
-    ch_agg <- components(ch_new,kern.n)
-
-    # Crop
-    ch_fin <- ch_agg[(ep+1):(side+ep),(ep+1):(side+ep),(ep+1):(h+ep)]
-
-    afr <- as.data.frame(table(ch_fin))
-    colnames(afr) <- c("ID","Size")
-    if(!is.null(pwidth) & !is.null(zstep)) afr$Size.micron <- afr$Size * pwidth^2 * zstep
-    afr$Img <- sub(paste0("_.*"),"",sub(".*/", "", ch_files[k]))
-    results[[k]] <- afr  
-    arrays[[k]] <- ch_fin
-  }
-
-  afrx <- do.call(rbind,results)
-
-  if(!is.null(naming)){
-    afrxn <- afrx
-    for(i in 1:length(naming)){
-      name.temp <- naming[[i]]
-      afrxn <- cbind(afrxn,NA)
-      for(j in 1:length(name.temp)){
-        afrxn[grep(name.temp[j],afrxn$Img),ncol(afrx)+i] <- name.temp[j]
-      }
-    }
-
-    colnames(afrxn) <- c(colnames(afrx),names(naming))
-
-  } else afrxn <- afrx
-
-  All <- list(Aggregates=afrxn,Arrays=arrays)
-
-  return(All)
-}
-
-
-
+#' Find 3D aggregates/clumps
+#'
+#' Function to group adjacent pixels in aggregates/clumps
+#' @param imgs The paths of array files; i.e. output from \code{loadIMG} or \code{findIMG} functions.
+#' @param channel Name of the channel to find aggregates in. Should be in the names of the array files
+#' @param kern.neighbour Numeric vector indicating range of neighbouring pixels to aggregate in the x,y,z directions. Has to be odd intergers. c(1,1,1) means no aggregating.
+#' @param kern.smooth Optional. Numeric vector indicating range of median smoothing in the x,y,z directions. Has to be odd intergers. c(1,1,1) means no smoothing.
+#' @param layers Optional. Should the function only look in a subset of layers. A list with lists of layers to use for each image. Can also be the output from \code{extract_layers} 
+#' @param pwidth Optional. Width of pixels in microns to calculate aggregate size in microns instead of pixels
+#' @param zstep Optional. z-step in microns to calculate aggregate size in microns instead of pixels
+#' @param naming Optional. Add metadata to the output dataframe by looking through names of array files. Should be a list of character vectors, each list element will be added as a variable. Example: naming=list(Time=c("T0","T1","T2")). The function inserts a variable called Time, and then looks through the names of the array files and inserts characters mathcing either T0, T1 or T2
+#' @keywords array image aggregate
+#' @return A list with two parts. First part is a dataframe with ID and size of aggregates and name of image, second part is a list of the arrays in which pixels are NA if empty or given a number indicating the aggregate ID
+#' @import mmand
+#' @export
+
+clumps <- function(imgs,channel,kern.neighbour=c(3,3,3),kern.smooth=NULL,layers=NULL,pwidth=NULL,zstep=NULL,naming=NULL) {
+
+  # Load image
+  ch_files <- imgs[grep(channel, imgs)]
+
+  # Lists for results
+  arrays <- list()
+  results <- list()
+
+  # For each replica
+  for(k in 1:length(ch_files)) {
+
+    message(paste("Running replica",k))
+
+    # Load
+    ch_t <- readRDS(ch_files[k])
+    side <- dim(ch_t)[1]
+    h <- dim(ch_t)[3]
+
+    # Subset layers
+    if(!is.null(layers[[k]])){
+      ch_t <- ch_t[,,layers[[k]]]
+    }
+
+    # Extend parameter
+    ep <- max(c(kern.neighbour,kern.smooth))
+
+    # Extend
+    ch_new <- array(NA, dim=c(side+(2*ep), side+(2*ep), h+(2*ep)))
+    ch_new[(ep+1):(side+ep),(ep+1):(side+ep),(ep+1):(h+ep)] <- ch_t 
+
+    # Smooth
+    if(!is.null(kern.smooth)) {
+      if(kern.smooth[1] %% 1 == 0 & kern.smooth[1] %% 2 != 0 &
+         kern.smooth[2] %% 1 == 0 & kern.smooth[2] %% 2 != 0 &
+         kern.smooth[3] %% 1 == 0 & kern.smooth[3] %% 2 != 0) {
+        kern.s <- kernelArray(array(1,dim=kern.smooth))
+        ch_new <- medianFilter(ch_new,kern.s)
+        ch_new[ch_new > 0] <- 1 } else stop("Kernel smooth has to be odd integers in all directions")
+    }
+
+    # Find aggregates
+    kern.n <- kernelArray(array(1,dim=kern.neighbour))
+    ch_agg <- components(ch_new,kern.n)
+
+    # Crop
+    ch_fin <- ch_agg[(ep+1):(side+ep),(ep+1):(side+ep),(ep+1):(h+ep)]
+
+    afr <- as.data.frame(table(ch_fin))
+    colnames(afr) <- c("ID","Size")
+    if(!is.null(pwidth) & !is.null(zstep)) afr$Size.micron <- afr$Size * pwidth^2 * zstep
+    afr$Img <- sub(paste0("_.*"),"",sub(".*/", "", ch_files[k]))
+    results[[k]] <- afr  
+    arrays[[k]] <- ch_fin
+  }
+
+  afrx <- do.call(rbind,results)
+
+  if(!is.null(naming)){
+    afrxn <- afrx
+    for(i in 1:length(naming)){
+      name.temp <- naming[[i]]
+      afrxn <- cbind(afrxn,NA)
+      for(j in 1:length(name.temp)){
+        afrxn[grep(name.temp[j],afrxn$Img),ncol(afrx)+i] <- name.temp[j]
+      }
+    }
+
+    colnames(afrxn) <- c(colnames(afrx),names(naming))
+
+  } else afrxn <- afrx
+
+  All <- list(Aggregates=afrxn,Arrays=arrays)
+
+  return(All)
+}
+
+
+