Fix imports

DESCRIPTION

@@ -12,15 +12,17 @@ License: GPL-3
 URL: https://github.com/ew367/CellPower
 Encoding: UTF-8
 LazyData: true
+LazyDataCompression: gzip
 Imports:
     doParallel,
+    parallel,
     dplyr,
     foreach,
     ggplot2,
     pwr,
     reshape2,
-    data.table
 Suggests: 
     knitr
+Depends: R (>= 2.10)
 VignetteBuilder: knitr
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1

NAMESPACE

@@ -4,3 +4,15 @@ export(calcDiff)
 export(calcProps)
 export(calcSamples)
 export(plotPower)
+import(doParallel)
+import(foreach)
+import(ggplot2)
+import(pwr)
+importFrom(dplyr,left_join)
+importFrom(dplyr,mutate)
+importFrom(dplyr,ntile)
+importFrom(parallel,clusterExport)
+importFrom(parallel,detectCores)
+importFrom(parallel,makeForkCluster)
+importFrom(reshape2,melt)
+importFrom(stats,complete.cases)

R/calcDiffFunction.r

@@ -10,10 +10,21 @@
 #'
 #' @return - Returns a list of matrices with one matrix per cell type/condition. Each matrix contains the power for each site (columns) and different mean differences (rows).
 #' @export
+#' @import pwr
+#' @import doParallel
+#' @import foreach
+#' @importFrom dplyr mutate
+#' @importFrom dplyr left_join
+#' @importFrom dplyr ntile
+#' @importFrom stats complete.cases
+#' @importFrom parallel detectCores
+#' @importFrom parallel makeForkCluster
+#' @importFrom parallel clusterExport
 #'
 #' @examples
 #' allSamples <- calcDiff(allSDs, dataType = "SDs")
-#' allSamples <- calcDiff(betas, phenoFile, nSamples = 100, dataType = "betaMatrix")
+
+# or for beta values (not supplied in this example): allSamples <- calcDiff(betas, phenoFile, nSamples = 100, dataType = "betaMatrix")
 
 
 
@@ -62,13 +73,13 @@ calcDiff <- function(betasOrSDs, pheno=NULL, nSamples=100, dataType, binSize = 5
 
   #define function to calculate power for each sample size
   powerCpG <- function(betasSD){
-    library(pwr)
+    require(pwr)
     power1 <- c()
     meanDiff <- seq(0.001, 0.1, 0.001)
 
 
     # bin data
-    betasSD <- as.data.frame(betasSD) %>% mutate(points_bin = ntile(betasSD, n=binSize))
+    betasSD <- as.data.frame(betasSD) %>% dplyr::mutate(points_bin = dplyr::ntile(betasSD, n=binSize))
 
     meanSDs <- c()
     for(j in unique(betasSD$points_bin)){
@@ -77,7 +88,7 @@ calcDiff <- function(betasOrSDs, pheno=NULL, nSamples=100, dataType, binSize = 5
     }
 
     for(diff in meanDiff){
-      power1 <- c(power1, pwr.t.test(d=diff/meanSDs,n=nSamples,sig.level=9e-8,type="two.sample",alternative="two.sided")$power)
+      power1 <- c(power1, pwr::pwr.t.test(d=diff/meanSDs,n=nSamples,sig.level=9e-8,type="two.sample",alternative="two.sided")$power)
     }
     power1 <- as.data.frame(matrix(power1, nrow=length(meanDiff), byrow = T))
 
@@ -89,10 +100,21 @@ calcDiff <- function(betasOrSDs, pheno=NULL, nSamples=100, dataType, binSize = 5
 
   print("Setting up parallel processors...")
 
-  no_cores <- detectCores() - 1
-  cl <- makeForkCluster(no_cores)
-  registerDoParallel(cl)
-  clusterExport(cl, c("allSDs", "nSamples", "binSize"), envir=environment())
+    require(dplyr)
+    require(foreach)
+    require(doParallel)
+
+chk <- Sys.getenv("_R_CHECK_LIMIT_CORES_", "")
+
+if (nzchar(chk) && chk == "TRUE") {
+    no_cores <- 2L
+} else {
+    no_cores <- parallel::detectCores() - 1
+}
+
+  cl <- parallel::makeForkCluster(no_cores)
+  doParallel::registerDoParallel(cl)
+  parallel::clusterExport(cl, c("allSDs", "nSamples", "binSize"), envir=environment())
 
   meanDiff <- seq(0.001, 0.1, 0.001)
   results <- foreach(i = colnames(allSDs)) %dopar% {powerCpG(allSDs[,i])}

R/calcPropsFunction.r

@@ -6,8 +6,11 @@
 #' @return - a dataframe containing the proportion of probes with power > 0.8 where rows are either mean differences or number of samples tested, and columns are each cell type
 #' @export
 #'
-#' @examples
-#' allProps <-calcProps(allSamples)
+
+# Example usage:
+
+# allSamples <- calcSamples(allSDs, dataType = "SDs")
+# allProps <-calcProps(allSamples)
 
 
 calcProps <- function(allCellRes){

R/calcSamplesFunction.r

@@ -10,10 +10,21 @@
 #'
 #' @return - Returns a list of matrices with one matrix per cell type/condition. Each matrix contains the power for each site (columns) and different sample sizes (rows).
 #' @export
+#' @import pwr
+#' @import doParallel
+#' @import foreach
+#' @importFrom dplyr mutate
+#' @importFrom dplyr left_join
+#' @importFrom dplyr ntile
+#' @importFrom stats complete.cases
+#' @importFrom parallel detectCores
+#' @importFrom parallel makeForkCluster
+#' @importFrom parallel clusterExport
 #'
 #' @examples
 #' allSamples <- calcSamples(allSDs, dataType = "SDs")
-#' allSamples <- calcSamples(betas, phenoFile, meanDiff = 5, dataType = "betaMatrix")
+#
+# or for beta values (not supplied in this example): allSamples <- calcSamples(betas, phenoFile, meanDiff = 5, dataType = "betaMatrix")
 
 
 calcSamples <- function(betasOrSDs, pheno=NULL, meanDiff=5, dataType, binSize = 500){
@@ -85,13 +96,13 @@ calcSamples <- function(betasOrSDs, pheno=NULL, meanDiff=5, dataType, binSize =
 
   #define function to calculate power for each sample size
   powerCpG <- function(betasSD){
-    library(pwr)
+    require(pwr)
     power1 <- c()
     Samples <- seq(nSamples/100, nSamples, nSamples/100)
     meandiffdec <- meanDiffDec
 
     # bin data
-    betasSD <- as.data.frame(betasSD) %>% mutate(points_bin = ntile(betasSD, n=binSize))
+    betasSD <- as.data.frame(betasSD) %>% dplyr::mutate(points_bin = dplyr::ntile(betasSD, n=binSize))
 
     meanSDs <- c()
     for(j in unique(betasSD$points_bin)){
@@ -102,7 +113,7 @@ calcSamples <- function(betasOrSDs, pheno=NULL, meanDiff=5, dataType, binSize =
 
     for(sample in Samples){
 
-      power1 <- c(power1, pwr.t.test(n=sample, d = meandiffdec/(meanSDs), sig.level = 9e-8,type="two.sample",alternative="two.sided")$power)
+      power1 <- c(power1, pwr::pwr.t.test(n=sample, d = meandiffdec/(meanSDs), sig.level = 9e-8,type="two.sample",alternative="two.sided")$power)
 
       }
 
@@ -115,10 +126,22 @@ calcSamples <- function(betasOrSDs, pheno=NULL, meanDiff=5, dataType, binSize =
 
   print("Setting up parallel processors...")
 
-    no_cores <- detectCores() - 1
-    cl <- makeForkCluster(no_cores)
-    registerDoParallel(cl)
-    clusterExport(cl, c("allSDs", "nSamples", "meanDiffDec", "binSize"), envir=environment())
+  require(dplyr)
+  require(foreach)
+  require(doParallel)
+
+
+chk <- Sys.getenv("_R_CHECK_LIMIT_CORES_", "")
+
+if (nzchar(chk) && chk == "TRUE") {
+    no_cores <- 2L
+} else {
+    no_cores <- parallel::detectCores() - 1
+}
+
+    cl <- parallel::makeForkCluster(no_cores)
+    doParallel::registerDoParallel(cl)
+    parallel::clusterExport(cl, c("allSDs", "nSamples", "meanDiffDec", "binSize"), envir=environment())
 
 
     samples <- seq(nSamples/100, nSamples, nSamples/100)

R/plotFunction.r

@@ -7,9 +7,13 @@
 #'
 #' @return - ggplot object with power curve plot
 #' @export
+#' @import ggplot2
+#' @importFrom reshape2 melt
 #'
-#' @examples
-#' myPlot <- plotPower(allProps, "samples")
+
+# Example usage:
+
+# myPlot <- plotPower(allProps, "samples")
 
 
 plotPower <- function(resFile, calcType){

vignettes/BrainPower-vignette.Rmd → vignettes/CellPower-vignette.Rmd

@@ -1,11 +1,11 @@
 ---
-title: "BrainPower-vignette"
+title: "CellPower-vignette"
 author:
   name: Emma Walker
   affiliation: University of Exeter
   email: E.M.Walker@exeter.ac.uk
 abstract: >
-  A tutorial for using the Brain Power package for power calculations of cell type specific array data
+  A tutorial for using the CellPower package for power calculations of cell type specific array data
 output: 
   BiocStyle::html_document:
     toc_float: true
@@ -15,18 +15,33 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-# How to use the BrainPower package
+```{r setup, include=FALSE}
 
-## Installing CETYGO
+if (!requireNamespace("BiocManager", quietly = TRUE)) {
+  install.packages("BiocManager")
+}
 
-The BrainPower package is available via GitHub
+# Now install BiocStyle
+BiocManager::install("BiocStyle")
+
+library(BiocStyle)
+
+```
+
+
+
+# How to use the CellPower package
+
+## Installing CellPower
+
+The CellPower package is available via GitHub
 and can be installed using the devtools package. However, there are some 
 pre-requistite packages that need to be installed from Bioconductor first.
 
 #### 
 
 
-Now we can install BrainPower direct from GitHub.
+Now we can install CellPower direct from GitHub.
 
 
 Once installed we can load the package.
@@ -58,6 +73,17 @@ The plot below shows how the proportion of CpGs with > 0.8 power changes as binS
 allSamples <- calcSamples(allSDs, meanDiff = 5, dataType = "SDs")
 
 
+#calculating the proportion of CpGs with > 0.8 power
+
+allProps <- calcProps(allSamples)
+
+
+
+#visualising the proportion of CpGs with > 0.8 power
+
+myPlot <- plotPower(allProps, "samples")
+
+
 ## add link to power calcualtions tutorial