rename package

DESCRIPTION

@@ -1,5 +1,5 @@
-Package: SSDRnaSeq
-Title: Semi-Supervise cellular deconvolution of Bulk RnaSeq using NMF and CiberSortx, DCQ or others
+Package: DICEPRO
+Title: Deconvolution with Iterative Completion for Estimating cellular Proportion from RNA-seq data
 Version: 0.0.0.1
 Authors@R: c(person("Kalidou", "BA", role = c("aut","cre"),
 	                  email = "kalidou.ba@u-bordeaux.fr"),

NAMESPACE

@@ -1,7 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(plot,SSDRnaSeq)
-export(SSDRnaSeq)
+S3method(plot,DICEPRO)
+export(DICEPRO)
 export(computPerf)
 export(generateProp)
 export(generate_ref_matrix)

R/SSDRnaSeq.R → R/DICEPRO.R

@@ -58,17 +58,16 @@
 #'
 #' @examples
 #' if(interactive()){
-#'
 #' simulation <- simulation(loi = "gauss", scenario = " ", bias = TRUE,
 #' nSample = 10, prop = NULL, nGenes = 50, nCellsType = 5)
 #' cellTypeOut <- sample(1:ncol(simulation$reference), 2)
 #' refDataIncomplet <- simulation$reference[,-cellTypeOut]
-#' results <- SSDRnaSeq(reference = refDataIncomplet, bulk = simulation$bulk,
+#' results <- DICEPRO(reference = refDataIncomplet, bulk = simulation$bulk,
 #' nIteration = 5, methodDeconv = "DCQ")
 #' print(results)
 #' }
 
-SSDRnaSeq <- function(reference, bulk, nIteration = 50, methodDeconv = "CSx", metric = "RRMSE",
+DICEPRO <- function(reference, bulk, nIteration = 50, methodDeconv = "CSx", metric = "RRMSE",
                       cibersortx_email = NULL, cibersortx_token = NULL) {
 
   stopifnot(methodDeconv %in% c("CSx", "DCQ", "CDSeq", "DeconRNASeq", "FARDEEP", "BayesPrism"))
@@ -78,26 +77,22 @@ SSDRnaSeq <- function(reference, bulk, nIteration = 50, methodDeconv = "CSx", me
   if(length(methodDeconv) > 1)
     methodDeconv <- methodDeconv[1]
 
-
   cellTypeName <- colnames(reference)
   geneIntersect <- intersect(rownames(reference), rownames(bulk))
 
   reference <- apply(reference[geneIntersect, ], 2, as.numeric)
   bulk <- apply(bulk[geneIntersect, ], 2, as.numeric)
   rownames(reference) <- rownames(bulk) <- geneIntersect
 
-
   # Initialize variables to store results and metrics
   matrixAbundances <- performs <- performs2plot <- opt <- NULL
 
   for (iterate_ in 0:nIteration) {
     message("Current iteration ++++++++++++++++++++++++++++++++ ", iterate_)
 
     out_Dec <- running_method(bulk, reference, methodDeconv,  cibersortx_email, cibersortx_token)
-
     bulkDeconv <- as.matrix(reference) %*% out_Dec
 
-
     matrixAbundances <- rbind(matrixAbundances, cbind.data.frame(t(out_Dec)[,cellTypeName], "Iterate" = iterate_))
 
     if(iterate_ > 0){
@@ -112,7 +107,6 @@ SSDRnaSeq <- function(reference, bulk, nIteration = 50, methodDeconv = "CSx", me
            (metric == "RRMSE" && performs[iterate_-1] - performs[iterate_] < 0 ))) {
         opt <- ifelse(iterate_ == nIteration, iterate_, iterate_ - 1)
         message("Convergence criteria Done with optimal criteria: ", opt, "\nBreaking the loop.")
-
         break
       }
     }
@@ -125,14 +119,13 @@ SSDRnaSeq <- function(reference, bulk, nIteration = 50, methodDeconv = "CSx", me
       colnames(unknownMat) <- paste0("Unknown_", col, "_", iterate_)
       reference <- cbind(reference, unknownMat)
     }
-
   }
-
   rownames(performs2plot) <- NULL
+
   results <- list("Prediction" = out_Dec, "Matrix_prediction" = matrixAbundances,
                   "New_signature" = reference, "Optimal_iteration" = opt,
                   "performs2plot" = performs2plot)
 
-  class(results) <- "SSDRnaSeq"
+  class(results) <- "DICEPRO"
   return(results)
 }

R/plot.SSDRnaSeq.R → R/plot.DICEPRO.R

@@ -9,14 +9,14 @@
 #'
 #' @return A combined plot showing the cell abundance heatmap and error plot.
 #'
-#'@method plot SSDRnaSeq
+#'@method plot DICEPRO
 #'@import patchwork
 #'
 #' @seealso Functions for generating individual plots: \code{\link{heatmap_abundances}}, \code{\link{metric_plot}}.
 #'
 #' @export
 
-plot.SSDRnaSeq <- function(x, ...){
+plot.DICEPRO <- function(x, ...){
 
   if(length(unique(x$Matrix_prediction$Iterate)) > 1){
     # Combine the heatmap and error between folds

tests/testthat.R

@@ -7,6 +7,6 @@
 # * https://testthat.r-lib.org/articles/special-files.html
 
 library(testthat)
-library(SSDRnaSeq)
+library(DICEPRO)
 
-test_check("SSDRnaSeq")
+test_check("DICEPRO")

tests/testthat/test_SSDRnaSeq.R

@@ -1,4 +1,4 @@
-test_that("SSDRnaSeq", {
+test_that("DICEPRO", {
   skip_on_cran()
   library(clusterGeneration)
   library(NMF)
@@ -13,6 +13,6 @@ test_that("SSDRnaSeq", {
   cellTypeOut <- sample(1:ncol(simulation$reference), 1)
   refDataIncomplet <- simulation$reference[,-cellTypeOut]
 
-  # res <- SSDRnaSeq(reference = refDataIncomplet, bulk = simulation$bulk, nIteration = 2,
+  # res <- DICEPRO(reference = refDataIncomplet, bulk = simulation$bulk, nIteration = 2,
   #                  methodDeconv = "DCQ")
 })

vignettes/test_real_datasets.Rmd

@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 
 
 ```{r setup}
-library(SSDRnaSeq)
+library(DICEPRO)
 library(ADAPTS)
 library(NMF)
 ```
@@ -30,20 +30,20 @@ library(NMF)
 # 
 # 
 # ```{r}
-# bulk <- SSDRnaSeq::GSE107011$gene_expression
-# res <- SSDRnaSeq::SSDRnaSeq(reference = LM22, bulk = bulk, nIteration = 2,  methodDeconv = "DCQ")
+# bulk <- DICEPRO::GSE107011$gene_expression
+# res <- DICEPRO::DICEPRO(reference = LM22, bulk = bulk, nIteration = 2,  methodDeconv = "DCQ")
 # ```
 #  
 #  
 #  
 # ```{r}
-# bulk <- SSDRnaSeq::GSE107572$gene_expression
-# res <- SSDRnaSeq::SSDRnaSeq(reference = LM22, bulk = bulk,  nIteration = 2,  methodDeconv = "DCQ")
+# bulk <- DICEPRO::GSE107572$gene_expression
+# res <- DICEPRO::DICEPRO(reference = LM22, bulk = bulk,  nIteration = 2,  methodDeconv = "DCQ")
 # ```
 # 
 # 
 # ```{r}
-# bulk <- SSDRnaSeq::GSE127813$gene_expression
-# res <- SSDRnaSeq::SSDRnaSeq(reference = LM22, bulk = bulk, nIteration = 2,  methodDeconv = "DCQ")
+# bulk <- DICEPRO::GSE127813$gene_expression
+# res <- DICEPRO::DICEPRO(reference = LM22, bulk = bulk, nIteration = 2,  methodDeconv = "DCQ")
 # ```
 

vignettes/test_simulation.Rmd

@@ -17,7 +17,7 @@ knitr::opts_chunk$set(
 
 
 ```{r setup}
-library(SSDRnaSeq)
+library(DICEPRO)
 library(ADAPTS)
 library(NMF)
 ```
@@ -94,7 +94,7 @@ names(cellTypeOut) <- paste0(as.character(percs * 100), "%")
 # 
 # for (i in names(cellTypeOut)) {
 #  refDataIncomplet <- reference[,-cellTypeOut[[i]]]
-#  listAllResult[[i]] <- SSDRnaSeq(reference = refDataIncomplet,
+#  listAllResult[[i]] <- DICEPRO(reference = refDataIncomplet,
 #                                  bulk = simulation$bulk,
 #                                  nIteration = nIteration,
 #                                  methodDeconv = "DCQ",