function imports

.travis.yml

@@ -1,8 +1,5 @@
 language: r
 
-services:
- - docker
-
 sudo: required
 bioc_required: true
 
@@ -18,15 +15,6 @@ r_binary_packages:
 bioc_packages:
  - mzR
 
-r_github_packages:
- - jasenfinch/binneR
-
-after_success:
-  - docker login -e="$DOCKER_EMAIL" -u="$DOCKER_USERNAME" -p="$DOCKER_PASSWORD"
-  - git clone https://github.com/jasenfinch/HRM-docker.git
-  - docker build -t jasenfinch/hrm-docker ./HRM-docker
-  - docker push jasenfinch/hrm-docker
-
 notifications:
   email:
     on_success: change

DESCRIPTION

@@ -8,9 +8,5 @@ Maintainer: Jasen Finch <[email protected]>
 Description: A spectral binning approach for high resolution flow infusion mass
     spectrometry data.
 License: GPL (>= 2)
-Depends: mzR, ggplot2, plyr
-Imports:
-    parallel,
-    shiny,
-    ggplot2
+Imports: parallel,shiny,ggplot2,plyr,mzR
 RoxygenNote: 5.0.1

NAMESPACE

@@ -1,14 +1,41 @@
 # Generated by roxygen2: do not edit by hand
 
-export(addMasses)
-export(colMasses)
-export(combScans)
-export(massMat)
 export(readFiles)
-export(sampProcess)
 export(viewSpectrum)
-import(ggplot2)
-import(mzR)
-import(parallel)
-import(plyr)
-import(shiny)
+importFrom(ggplot2,aes_string)
+importFrom(ggplot2,geom_line)
+importFrom(ggplot2,geom_segment)
+importFrom(ggplot2,geom_text)
+importFrom(ggplot2,geom_vline)
+importFrom(ggplot2,ggplot)
+importFrom(ggplot2,ggtitle)
+importFrom(ggplot2,theme_bw)
+importFrom(ggplot2,xlab)
+importFrom(ggplot2,ylab)
+importFrom(mzR,header)
+importFrom(mzR,openMSfile)
+importFrom(mzR,peaks)
+importFrom(mzR,runInfo)
+importFrom(parallel,makeCluster)
+importFrom(parallel,parLapplyLB)
+importFrom(parallel,stopCluster)
+importFrom(plyr,ldply)
+importFrom(shiny,fileInput)
+importFrom(shiny,fluidPage)
+importFrom(shiny,fluidRow)
+importFrom(shiny,mainPanel)
+importFrom(shiny,numericInput)
+importFrom(shiny,plotOutput)
+importFrom(shiny,radioButtons)
+importFrom(shiny,reactive)
+importFrom(shiny,renderPlot)
+importFrom(shiny,renderUI)
+importFrom(shiny,shinyApp)
+importFrom(shiny,sidebarLayout)
+importFrom(shiny,sidebarPanel)
+importFrom(shiny,sliderInput)
+importFrom(shiny,tags)
+importFrom(shiny,textInput)
+importFrom(shiny,titlePanel)
+importFrom(shiny,uiOutput)
+importFrom(stats,aggregate)

R/addMasses.R

@@ -1,17 +1,16 @@
-#' Add in masses with zero intensity if not present in a peak matrix in a peak list
-#' @name addMasses
-#' @description This function will add rows to a peak matrix of missing m/z identified within the supplied peak list to ensure equal matrix row lengths for further processing  
-#' @param x A list containing peak matrices for each scan
-#' @return A list containing peak matrices with added 0 values
-#' @author Jasen Finch
-#' @export
+# Add in masses with zero intensity if not present in a peak matrix in a peak list
+# @name addMasses
+# @description This function will add rows to a peak matrix of missing m/z identified within the supplied peak list to ensure equal matrix row lengths for further processing  
+# @param x A list containing peak matrices for each scan
+# @return A list containing peak matrices with added 0 values
+# @author Jasen Finch
 
 addMasses <- function(x){
 	masses <- colMasses(x)
 	y.1 <- NULL
-	for (i in 1:length(x)){
+	for (i in 1:length(x)) {
 		y <- data.frame(x[[i]])
-		mat <- matrix(0,nrow=length(masses),ncol=2)
+		mat <- matrix(0,nrow = length(masses),ncol = 2)
 		names(mat) <- c("mz","intensity")
 		z <- masses %in% y[,1]
 		mat[,1] <- masses

R/colMasses.R

@@ -1,14 +1,13 @@
-#' Collate all masses present in the supplied list 
-#' @name colMasses
-#' @description Collate all masses present across all scans in the supplied peak list, into a single reference vector.
-#' @param x A peak list containing peak matrices for each scan
-#' @return A vector containing all the masses present in the supplied peak list
-#' @author Jasen Finch
-#' @export
+# Collate all masses present in the supplied list 
+# @name colMasses
+# @description Collate all masses present across all scans in the supplied peak list, into a single reference vector.
+# @param x A peak list containing peak matrices for each scan
+# @return A vector containing all the masses present in the supplied peak list
+# @author Jasen Finch
 
 colMasses <- function(x){ 
  z <- NULL
-  for (i in 1:length(x)){
+  for (i in 1:length(x)) {
     y <- data.frame(x[[i]])
     y.1 <- as.numeric(y[,1])
     z <- c(z,y.1)  

R/combScans.R

@@ -1,48 +1,47 @@
-#' Combine scan ranges and retrieve given scans
-#' @name combScans
-#' @description A function that will split a peak list into appropriate modes, retrieve peak matrices of the supplied relevant scans and combine scan ranges. 
-#' @param x A peak list containing peak matrices for each scan
-#' @param scans A vector of scan numbers that should be retrieved 
-#' @param sranges A list of vectors containing the scan events present 
-#' @param modes A vector of strings denoting mode names
-#' @return A list containing peak lists for the relevant scans with combined scan ranges for each present mode. 
-#' @author Jasen Finch
-#' @export
-#' @import plyr
+# Combine scan ranges and retrieve given scans
+# @name combScans
+# @description A function that will split a peak list into appropriate modes, retrieve peak matrices of the supplied relevant scans and combine scan ranges. 
+# @param x A peak list containing peak matrices for each scan
+# @param scans A vector of scan numbers that should be retrieved 
+# @param sranges A list of vectors containing the scan events present 
+# @param modes A vector of strings denoting mode names
+# @return A list containing peak lists for the relevant scans with combined scan ranges for each present mode. 
+# @author Jasen Finch
+#' @importFrom plyr ldply
 
 combScans <- function(x,scans,sranges,modes){
 	# separate scans based on modes
 	pat <- unlist(lapply(modes,function(x,sranges){
 		rep(x,length(sranges))
-	},sranges=sranges))
+	},sranges = sranges))
 	pat <- rep(pat,length(x)/length(pat))
 	pos.neg <- lapply(modes,function(y,x,pat){
-		x <- x[which(pat==y)]
+		x <- x[which(pat == y)]
 		return(x)
-	},pat=pat,x=x)
+	},pat = pat,x = x)
 	# separate scan ranges and keep only relevant scans
-	pat.1 <- unlist(lapply(seq(1,length(pos.neg[[1]])/length(sranges)),rep,times=length(sranges)))
+	pat.1 <- unlist(lapply(seq(1,length(pos.neg[[1]])/length(sranges)),rep,times = length(sranges)))
 	pos.neg <- lapply(pos.neg,function(x,pattern){
 		y <- list()
-		for (i in unique(pattern)){
-			y[[i]] <- x[which(pattern==i)]
+		for (i in unique(pattern)) {
+			y[[i]] <- x[which(pattern == i)]
 		}
 		return(y)
-	},pattern=pat.1)
+	},pattern = pat.1)
 	pos.neg <- lapply(pos.neg,function(x,scans){
 		return(x[scans])
-	},scans=scans)
+	},scans = scans)
 	# combine scan ranges into single lists, boundary always equal to upper limit
 	pos.neg <- lapply(pos.neg,function(x,sranges){
 		return(lapply(x,function(y,sranges){
-			for (i in 1:length(y)){
+			for (i in 1:length(y)) {
 				z <- y[[i]]
-				y[[i]] <- z[which(z[,1]>sranges[[i]][1] & z[,1] <= sranges[[i]][2]),]
+				y[[i]] <- z[which(z[,1] > sranges[[i]][1] & z[,1] <= sranges[[i]][2]),]
 			}
 			y <- ldply(y,data.frame)
 			colnames(y) <- c("mz","intensity")
 			return(y)
-		},sranges=sranges))},sranges=sranges)
+		},sranges = sranges))},sranges = sranges)
 	names(pos.neg) <- names(mode)
 	return(pos.neg)
 	}

R/massMat.R

@@ -1,20 +1,19 @@
-#' build an intensity matrix for supplied peak list
-#' @name massMat
-#' @description build and intensity matrix for a supplied peak list. Can be used to build intensity matrices for combining either scans within a sample of multiple samples. 
-#' @param x A peak list containing peak matrices for each scan of equal row length
-#' @return An intensity matrix with as many rows as peak matrices in peak list
-#' @author Jasen Finch
-#' @export
+# build an intensity matrix for supplied peak list
+# @name massMat
+# @description build and intensity matrix for a supplied peak list. Can be used to build intensity matrices for combining either scans within a sample of multiple samples. 
+# @param x A peak list containing peak matrices for each scan of equal row length
+# @return An intensity matrix with as many rows as peak matrices in peak list
+# @author Jasen Finch
 
 massMat <- function(x){ 
 	z <- data.frame(x[1])
-	mat <- matrix(0,nrow=length(x),ncol=length(z[,1]))
-	for (i in 1:length(x)){
+	mat <- matrix(0,nrow = length(x),ncol = length(z[,1]))
+	for (i in 1:length(x)) {
 		y <- data.frame(x[i])
 		y <- t(y)
  		y <- y[2,]
 		mat[i,] <- y
 	}
 	colnames(mat) <- z[,1]
-	return (mat)
+	return(mat)
 }

R/readFiles.R

@@ -10,48 +10,31 @@
 #' @return A list containing peak lists for the relevant scans with combined scan ranges for each present mode in the data file. 
 #' @author Jasen Finch
 #' @export
-#' @import parallel
-readFiles <- function(files,dp,scans,sranges=list(c(50,1000)),modes=c("p","n"),nCores=2){ # for data collected in both modes
-  if(nCores<2){
-    pl <- lapply(files,sampProcess,scans=scans,dp=dp,sranges=sranges,modes=modes)
-    # split modes
-    pos.neg <- list()
-    for (i in 1:length(modes)){
-    	pos.neg[[i]] <- lapply(pl,function(x,mode){return(x[mode])},mode=modes[i])
-    	gc()
-    }  
-    # add in masses to get equal lengths for each sample
-    for (i in 1:length(pos.neg)){
-			pos.neg[[i]] <- addMasses(pos.neg[[i]])
-			gc()
-    }
-  	# build  intensity matrix
-    for (i in 1:length(pos.neg)){
-  		pos.neg[[i]] <- massMat(pos.neg[[i]])	
-  		gc()
-    }
-  }else{
-    clust = makeCluster(nCores, type="PSOCK") 
-    clusterExport(clust,c(ls("package:binneR"),ls("package:mzR"),ls("package:Rcpp"),ls("package:plyr")))
-		pl <- parLapplyLB(clust,files ,fun= sampProcess,scans=scans,dp=dp,sranges=sranges,modes=modes)	 
-		# split modes
-    pos.neg <- list()
-    for (i in 1:length(modes)){
-    	pos.neg[[i]] <- lapply(pl,function(x,mode){return(x[mode])},mode=modes[i])
-    }  
-		pl <- NULL
-		gc()
-  	# add in masses to get equal lengths for each sample
-		pos.neg <- parLapplyLB(clust,pos.neg,fun=addMasses)	
-		gc()
-  	# build  intensity matrix
-  	pos.neg <- parLapplyLB(clust,pos.neg,fun=massMat)
-  	stopCluster(clust)
-  }
-  for (i in 1:length(modes)){
-  	colnames(pos.neg[[i]]) <- paste(modes[i],colnames(pos.neg[[i]]),sep="")
-  }
-  names(pos.neg) <- modes
-  gc()
-  return(pos.neg)
+#' @importFrom parallel makeCluster parLapplyLB stopCluster
+#' @examples 
+#' res <- readFiles(list.files(system.file('mzXML',package = 'binneR'),
+#'                  full.names=TRUE),dp = 2,scans = 6:17)
+
+readFiles <- function(files,dp, scans, sranges = list(c(50,1000)), modes = c("n","p"), nCores = 1){ # for data collected in both modes
+	clust = makeCluster(nCores, type = "PSOCK") 
+	pl <- parLapplyLB(clust,files ,fun = sampProcess,scans = scans,dp = dp,sranges = sranges,modes = modes)	 
+	# split modes
+	pos.neg <- list()
+	for (i in 1:length(modes)) {
+		pos.neg[[i]] <- lapply(pl,function(x,mode){return(x[mode])},mode = modes[i])
+	}  
+	pl <- NULL
+	gc()
+	# add in masses to get equal lengths for each sample
+	pos.neg <- parLapplyLB(clust,pos.neg,fun = addMasses)	
+	gc()
+	# build  intensity matrix
+	pos.neg <- parLapplyLB(clust,pos.neg,fun = massMat)
+	stopCluster(clust)
+	for (i in 1:length(modes)) {
+		colnames(pos.neg[[i]]) <- paste(modes[i],colnames(pos.neg[[i]]),sep = "")
+	}
+	names(pos.neg) <- modes
+	gc()
+	return(pos.neg)
 }  

R/sampProcess.R

@@ -1,15 +1,15 @@
-#'  process a single data file
-#' @name sampProcess
-#' @description Apply spectral binning on a single data file.
-#' @param file File path of converted data file
-#' @param scans A vector of scan numbers that should be retrieved 
-#' @param dp An integer denoting the number of decimal places for spectral binning
-#' @param sranges A list of vectors containing the scan events present 
-#' @param modes A vector of strings denoting mode names
-#' @return A list containing peak lists for the relevant scans with combined scan ranges for each present mode in the data file. 
-#' @author Jasen Finch
-#' @export
-#' @import mzR
+#  process a single data file
+# @name sampProcess
+# @description Apply spectral binning on a single data file.
+# @param file File path of converted data file
+# @param scans A vector of scan numbers that should be retrieved 
+# @param dp An integer denoting the number of decimal places for spectral binning
+# @param sranges A list of vectors containing the scan events present 
+# @param modes A vector of strings denoting mode names
+# @return A list containing peak lists for the relevant scans with combined scan ranges for each present mode in the data file. 
+# @author Jasen Finch
+#' @importFrom mzR openMSfile peaks
+#' @importFrom stats aggregate
 
 sampProcess <- function(file,scans,dp,sranges,modes){
 	aa <- openMSfile(file)
@@ -23,8 +23,8 @@ sampProcess <- function(file,scans,dp,sranges,modes){
 			# aggregate bins to give ion totals
 			y <- aggregate(y[,"intensity"],list(y[,"mz"]),sum)
 			return(y)
-			},dp=dp))
-		},dp=dp)
+			},dp = dp))
+		},dp = dp)
 	# add zeros for missing bins in each scan
 	pl <- lapply(pl,addMasses)
 	pl <- lapply(pl,massMat)
@@ -34,7 +34,7 @@ sampProcess <- function(file,scans,dp,sranges,modes){
 		return(x)
 		})
 	pl <- lapply(pl,function(x){
-		x <- data.frame(mz=as.numeric(as.character(names(x))),intensity=x,row.names=NULL)
+		x <- data.frame(mz = as.numeric(as.character(names(x))),intensity = x,row.names = NULL)
 		return(x)
 		})
 	names(pl) <- modes