Version 1.0.0

DESCRIPTION

@@ -2,8 +2,8 @@ Package: espresso
 Type: Package
 Title: Enhanced spatial reconstruction by stochastic self-organizing
         map
-Version: 0.2.17
-Date: 2022-01-13
+Version: 1.0.0
+Date: 2022-01-21
 Authors@R: person("Tomoya", "Mori", role = c("aut", "cre"), email = "[email protected]")
 Description: This is a package for domain estimation of cells only from gene expression profile based on graph-structured stochastic self-organizing map (GraphSOM) and Markov chain Monte Carlo (MCMC) method.
 License: GPL-3
@@ -18,6 +18,6 @@ LazyData: true
 Suggests: knitr, rmarkdown
 VignetteBuilder: knitr
 NeedsCompilation: no
-Packaged: 2022-01-14 01:52:59 UTC; tmori
+Packaged: 2022-01-21 06:50:12 UTC; tmori
 Author: Tomoya Mori [aut, cre]
 Maintainer: Tomoya Mori <[email protected]>

NEWS.md

@@ -1,6 +1,9 @@
+## 1.0.0
+* Add an option `version` to `graphSOM` and `rxmcmc` functions in order to enable us to perform SOM computations based on the previous version (= 0.2.17).
+
 ## 0.2.17
 * Add options `size_d` and `size_s` to `plotDistMap` function.
-# Add options `color_by_group`, `grpcol`, and `magnify` to `plotUMAP` function.
+* Add options `color_by_group`, `grpcol`, and `magnify` to `plotUMAP` function.
 
 ## 0.2.16
 * Replace graphSOM and rxmcmc functions of v.0.2.15 with those of v.0.2.14

R/boruta.R

@@ -14,7 +14,7 @@
 #' 
 selectFeatures <- function(obj, maxRuns = 500, decision = "nr", seed = NULL) {
   if (!is.null(seed)) {
-    set.seed(seed)
+    set.seed(seed, kind = "Mersenne-Twister")
   }
   X <- obj@exprs
   Y <- as.factor(obj@asgmt[, "domain"])

R/graphsom.R

@@ -590,7 +590,7 @@ initGraphSOM <- function(obj, nsamples = NULL, rept = 1,
                          stochastic = TRUE, rmin = 0.5, rmax = 1.0, nmin = 1, nmax = NULL,
                          map_method = "sample", bmu_method = "min", coef = 1.0, swap = TRUE, seed = NULL) {
   if (!is.null(seed)) {
-    set.seed(as.numeric(seed))
+    set.seed(as.numeric(seed), kind = "Mersenne-Twister")
   }
   obj <- .genSampleSets(obj = obj, nsamples = nsamples, rept = as.integer(rept))
   obj <- .initGraphSOM(obj = obj, lsteps = as.integer(lsteps), radius = radius, 
@@ -607,12 +607,22 @@ initGraphSOM <- function(obj, nsamples = NULL, rept = 1,
 #' @param gset Gene set.
 #' @param seed Random seed.
 #' @param verbose Whether to show messages.
+#' @param version Character that specifies on which previous version GraphSOM computation should be performed. (e.g., "0.2.17")
 #' @return \code{espresso} object
 #' @export 
 #' 
-graphSOM <- function(obj, gset = NULL, seed = NULL, verbose = TRUE) {
-  if (!is.null(seed)) {
-    set.seed(seed)
+graphSOM <- function(obj, gset = NULL, seed = NULL, verbose = TRUE, version = NULL) {
+  if (is.null(version)) {
+    if (!is.null(seed)) {
+      set.seed(seed, kind = "L'Ecuyer-CMRG")
+    }
+  } else if (version == "0.2.17") {
+    if (!is.null(seed)) {
+      set.seed(seed, kind = "Mersenne-Twister")
+    }
+  } else {
+    stop(paste("version", ver, "is not available."))
+    return(obj)
   }
   if (!is.null(gset)) {
     obj@gset <- gset[!is.na(match(gset, colnames(obj@exprs)))]

R/rxmcmc.R

@@ -207,6 +207,7 @@
 #' @param n_repl Number of replicas (default: \code{detectCores()}).
 #' @param n_ig Number of initial genes randomly selected for MCMC.
 #' @param fact Scaling factor for computing selection probability.
+#' @param version Character that specifies on which previous version GraphSOM computation should be performed. (e.g., "0.2.17")
 #' @import doParallel
 #' @import parallel
 #' @import foreach
@@ -215,16 +216,20 @@
 #' @export 
 #'
 rxmcmc <- function(obj, gset = NULL, temp = 1.0, itr = 10, k = 1, seed = NULL, 
-                   n_cl = detectCores(), n_ex = 10, n_repl = detectCores(), n_ig = 3, fact = NULL) {
+                   n_cl = detectCores(), n_ex = 10, n_repl = detectCores(), n_ig = 3, fact = NULL, version = NULL) {
   if (!is.null(seed)) {
-    set.seed(seed)
+    set.seed(seed, kind = "Mersenne-Twister")
   } else {
     seed <- 0
   }
   if (is.null(gset)) {
     warning("Input `gset` to be optimized.")
     return(obj)
   }
+  if (version != "0.2.17" && !is.null(version)) {
+    stop(paste("version", version, "is not available."))
+    return(obj)
+  }
   gset0 <- gset
   exist <- match(gset, colnames(obj@exprs))
   gset <- colnames(obj@exprs)[exist[!is.na(exist)]]
@@ -283,13 +288,24 @@ rxmcmc <- function(obj, gset = NULL, temp = 1.0, itr = 10, k = 1, seed = NULL,
       }
       n_sets <- length(cand_sets)
       res <- .generateDataFrameForRXMCMC(1, n_sets)[[1]]
-      for (l in 1:n_sets) {
-        res_tmp <- graphSOM(obj, gset = cand_sets[[l]], verbose = FALSE)
-        res[l, ] <- c(as.numeric(unlist(res_tmp@summary)), length(cand_sets[[l]]))
-        map_tmp[[l]] <- res_tmp@map
-        bmu_tmp[[l]] <- res_tmp@bmu
-        score_tmp[[l]] <- res_tmp@score
-        summary_tmp[[l]] <- res_tmp@summary
+      if (is.null(version)) {
+        for (l in 1:n_sets) {
+          res_tmp <- graphSOM(obj, gset = cand_sets[[l]], verbose = FALSE, seed = seed)
+          res[l, ] <- c(as.numeric(unlist(res_tmp@summary)), length(cand_sets[[l]]))
+          map_tmp[[l]] <- res_tmp@map
+          bmu_tmp[[l]] <- res_tmp@bmu
+          score_tmp[[l]] <- res_tmp@score
+          summary_tmp[[l]] <- res_tmp@summary
+        }
+      } else if (version == "0.2.17"){
+        for (l in 1:n_sets) {
+          res_tmp <- graphSOM(obj, gset = cand_sets[[l]], verbose = FALSE, version = "0.2.17")
+          res[l, ] <- c(as.numeric(unlist(res_tmp@summary)), length(cand_sets[[l]]))
+          map_tmp[[l]] <- res_tmp@map
+          bmu_tmp[[l]] <- res_tmp@bmu
+          score_tmp[[l]] <- res_tmp@score
+          summary_tmp[[l]] <- res_tmp@summary
+        }
       }
       probs <- .definePriorDistr(res, fact)
       idx <- .sampleGeneSet(probs)

R/umap.R

@@ -8,7 +8,7 @@
 #' @importFrom uwot umap
 #' @export
 runUMAP <- function(obj, umap_param = NULL, gset = NULL, seed = 0) {
-  set.seed(as.numeric(seed))
+  set.seed(as.numeric(seed), kind = "Mersenne-Twister")
   if (!is.null(gset)) {
     gset <- gset[!is.na(match(gset, colnames(obj@exprs)))]
     genes <- gset

README.md

@@ -1,5 +1,9 @@
 # eSPRESSO
 
+21-Jan-2022: espresso 1.0.0 released.
+
+<br/>
+
 `espresso` is a package for domain estimation of cells only from gene expression profile based on graph-structured stochastic self-organizing map (graph-SOM) and Markov chain Monte Carlo (MCMC) method.
 
 <img src="img/fig1.png">
@@ -18,5 +22,5 @@ install_github("tmorikuicr/espresso")
 ```
 
 ## Usage
-Refer to the following documentation for the usage of eSPRESSO package.  
+Refer to the following documentation for the usage of `espresso` package.  
 [Vignette](https://tmorikuicr.github.io/espresso/)