1. OncoSimulR
  2. Commit fc38a87533e0bce23c6602d2efee5006f6f24edc
Browse code

v.2.5.2 - Lots and lots of addition to vignette including benchmarks. - Diversity of sampled genotypes. - Genotyping error can be added in samplePop. - LOD and POM (lines of descent, path of maximum, sensu Szendro et al.). - simOGraph can also out rT data frames. - Better (and better explained) estimates of simulation error for McFL.

git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/OncoSimulR@124982 bc3139a8-67e5-0310-9ffc-ced21a209358

Ramon Diaz-Uriarte authored on 10/12/2016 16:05:05
Showing 36 changed files
DESCRIPTION
History View file @ fc38a87
... ... 
@@ -1,8 +1,8 @@
1 1 
 Package: OncoSimulR
2 2 
 Type: Package
3 3 
 Title: Forward Genetic Simulation of Cancer Progression with Epistasis
4 
-Version: 2.5.1
5 
-Date: 2016-11-12
4 
+Version: 2.5.2
5 
+Date: 2016-12-10
6 6 
 Authors@R: c(person("Ramon", "Diaz-Uriarte", role = c("aut", "cre"),
7 7 
 		     email = "rdiaz02@gmail.com"),
8 8 
 	      person("Mark", "Taylor", role = "ctb", email = "ningkiling@gmail.com"))
NAMESPACE
History View file @ fc38a87
... ... 
@@ -11,6 +11,8 @@ export("oncoSimulPop", "oncoSimulIndiv", "samplePop",
11 11 
        "plotFitnessLandscape",
12 12 
        "to_Magellan",
13 13 
        "sampledGenotypes"
14 
+       , "POM", "LOD"
15 
+       , "diversityPOM", "diversityLOD"
14 16 
        )
15 17
16 18 
 S3method(plot, oncosimul)
... ... 
@@ -23,6 +25,19 @@ S3method(plot, fitnessEffects)
23 25 
 S3method(plot, genotype_fitness_matrix)
24 26 
 S3method(plot, evalAllGenotypes)
25 27 
 S3method(plot, evalAllGenotypesMut)
28 
+S3method(print, sampledGenotypes)
29 
+
30 
+S3method(POM, oncosimul2)
31 
+S3method(POM, oncosimulpop)
32 
+S3method(LOD, oncosimul2)
33 
+S3method(LOD, oncosimulpop)
34 
+
35 
+
36 
+## S3method(summary, oncosimul_lod_list)
37 
+## S3method(summary, oncosimul_pom_list)
38 
+
39 
+
40 
+
26 41
27 42 
 import(ggplot2)
28 43 
 importFrom("ggrepel", geom_text_repel, geom_label_repel)
R/LOD_POM.R
History View file @ fc38a87
29 44 
new file mode 100644
... ... 
@@ -0,0 +1,168 @@
1 
+## Copyright 2016 Ramon Diaz-Uriarte
2 
+
3 
+## This program is free software: you can redistribute it and/or modify
4 
+## it under the terms of the GNU General Public License as published by
5 
+## the Free Software Foundation, either version 3 of the License, or
6 
+## (at your option) any later version.
7 
+
8 
+## This program is distributed in the hope that it will be useful,
9 
+## but WITHOUT ANY WARRANTY; without even the implied warranty of
10 
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
11 
+## GNU General Public License for more details.
12 
+
13 
+## You should have received a copy of the GNU General Public License
14 
+## along with this program.  If not, see <http://www.gnu.org/licenses/>.
15 
+
16 
+
17 
+
18 
+## Functions to obtain LOD and POM similar to Szendro et al., 2014, PNAS.
19 
+genot_max <- function(x) {
20 
+    x$GenotypesLabels[which.max(x$pops.by.time[nrow(x$pops.by.time), -1])]
21 
+}
22 
+
23 
+LOD.internal <- function(x) {
24 
+    ## Not identical to LOD of Szendro because:
25 
+
26 
+    ##  a) I think we want all paths, not just their single LOD, which I
27 
+    ##  think they might use out of convenience.
28 
+
29 
+    ##  b) For me it is a mess, a complicated mess, to use their LOD as
30 
+    ##  they define it and there are many ambiguities in how to define it
31 
+    ##  in continuous time.
32 
+
33 
+    ## This also means that single simulation might yield multiple LODs
34 
+
35 
+    ## keepEvents is FALSE to make this object as small as possible.
36 
+    if(is.null(x$pops.by.time)) {
37 
+        warning("Missing needed components. This might be a failed simulation.",
38 
+                " Returning NA.")
39 
+        return(list(all_paths = NA, lod_single = NA))
40 
+    }
41 
+    pc <- phylogClone(x, keepEvents = FALSE)
42 
+
43 
+    if((length(pc) == 1) && (is.na(pc))) {
44 
+        return(list(all_paths = NA,
45 
+                    lod_single = "No_descendants"))
46 
+    }
47 
+    pcg <- pc$graph
48 
+    end <- genot_max(x)
49 
+    all_paths <- igraph::all_simple_paths(pcg, from = "", to = end, mode = "out")
50 
+    ## the next is partially redundant
51 
+    ## graph_to_end <- igraph::make_ego_graph(pcg, order = 1e9, nodes = end,
52 
+    ##                                        mode = "in")
53 
+    ## if(length(graph_to_end) != 1) stop("length(graph_to_end) > 1")
54 
+    ## I am not sure if I should keep the last one. Redundant
55 
+
56 
+    ## This gives a single path and it is the first entry into each
57 
+    ## destination. But we do not check there is no extinction afterwards.
58 
+    ## The closest to the single Szendro LOD
59 
+    if(end == "") {
60 
+        ## Max is WT
61 
+        lod_single <- "WT_is_end"
62 
+    } else {
63 
+        singlep <- pc$df
64 
+        singlep[, 1] <- as.character(singlep[, 1])
65 
+        singlep[, 2] <- as.character(singlep[, 2])
66 
+        singlep <- singlep[ do.call(order, singlep[, c(2, 3)]), ]
67 
+        singlep <- singlep[!duplicated(singlep[, 2]), ]
68 
+        gsingle <- igraph::graph_from_data_frame(singlep)
69 
+        lod_single <- igraph::all_simple_paths(gsingle, from = "", to = end, mode = "out")
70 
+        if(length(lod_single) != 1) stop("lod_single != 1")
71 
+    }
72 
+    return(list(all_paths = all_paths,
73 
+                lod_single = lod_single[[1]])) ##, graph_to_end = graph_to_end[[1]]))
74 
+                ## graph_phylog_clone = pcg))
75 
+}
76 
+
77 
+
78 
+POM.internal <- function(x) {
79 
+    if(is.null(x$pops.by.time)) {
80 
+        warning("Missing needed components. This might be a failed simulation.",
81 
+                " Returning NA.")
82 
+        return(NA)
83 
+    }
84 
+    x$GenotypesLabels[rle(apply(x$pops.by.time[, -1, drop = FALSE], 1, which.max))$values]
85 
+}
86 
+
87 
+## First do, over a set of simulations, sim:
88 
+## l_lod_single <- mclapply(sim, LODs)
89 
+## l_poms <- mclapply(sim, POM)
90 
+
91 
+
92 
+diversityLOD <- function(llod) {
93 
+    nn <- names(llod[[1]])
94 
+    if( is_null_na(nn) ||
95 
+        !(nn == c("all_paths", "lod_single")))
96 
+        stop("Object must be a list of LODs")
97 
+    pathstr <- unlist(lapply(llod, function(x) paste(names(x$lod_single),
98 
+                                                     collapse = "_")))
99 
+    shannonI(table(pathstr))
100 
+}
101 
+
102 
+diversityPOM <- function(lpom) {
103 
+    if(!inherits(lpom, "list"))
104 
+        stop("Object must be a list of POMs")
105 
+    ## if(!inherits(x, "oncosimul_lod_list"))
106 
+    ##     stop("This is not a list of POMs")
107 
+    pomstr <- unlist(lapply(lpom, function(x) paste(x, collapse = "_")))
108 
+    shannonI(table(pomstr))
109 
+}
110 
+
111 
+## a legacy
112 
+diversity_POM <- diversityPOM
113 
+diversity_LOD <- diversityLOD
114 
+
115 
+## POM.oncosimul2 <- POM.internal
116 
+## LOD2.oncosimul2 <- LOD.internal
117 
+
118 
+POM <- function(x) {
119 
+    UseMethod("POM", x)
120 
+}
121 
+
122 
+LOD <- function(x) {
123 
+    UseMethod("LOD", x)
124 
+}
125 
+
126 
+POM.oncosimul2 <- function(x) return(POM.internal(x))
127 
+LOD.oncosimul2 <- function(x) return(LOD.internal(x))
128 
+
129 
+POM.oncosimulpop <- function(x) return(lapply(x, POM.internal))
130 
+LOD.oncosimulpop <- function(x) return(lapply(x, LOD.internal))
131 
+
132 
+## POM.oncosimul2 <- function(x) {
133 
+##     out <- POM.internal(x)
134 
+##     class(out) <- c(class(out), "oncosimul_pom")
135 
+##     return(out)
136 
+## }
137 
+
138 
+## LOD.oncosimul2 <- function(x) {
139 
+##     out <- LOD.internal(x)
140 
+##     class(out) <- c(class(out), "oncosimul_lod")
141 
+##     return(out)
142 
+## }
143 
+
144 
+
145 
+## POM.oncosimulpop <- function(x) {
146 
+##     out <- lapply(x, POM.internal)
147 
+##     class(out) <- c(class(out), "oncosimul_pom_list")
148 
+##     return(out)
149 
+## }
150 
+
151 
+## LOD.oncosimulpop <- function(x) {
152 
+##     out <- lapply(x, LOD.internal)
153 
+##     class(out) <- c(class(out), "oncosimul_lod_list")
154 
+##     return(out)
155 
+## }
156 
+
157 
+
158 
+## summary.oncosimul_lod_list <- function(x) {
159 
+##     cat("List of ", length(x), " simulations\n.")
160 
+##     cat("Shannon's diversity (entropy) = ", diversityLOD(x), "\n")
161 
+## }
162 
+
163 
+## summary.oncosimul_pom_list <- function(x) {
164 
+##     cat("List of ", length(x), " simulations\n.")
165 
+##     cat("Shannon's diversity (entropy) = ", diversityPOM(x), "\n")
166 
+## }
167 
+
168 
+
R/OncoSimulR.R
History View file @ fc38a87
... ... 
@@ -258,12 +258,26 @@ oncoSimulSample <- function(Nindiv,
258 258 
     }
259 259 
 }
260 260
261 
+add_noise <- function(x, properr) {
262 
+    if(properr <= 0) {
263 
+        return(x)
264 
+    }
265 
+    else {
266 
+        if(properr > 1)
267 
+            stop("Proportion with error cannot be > 1")
268 
+        nn <- prod(dim(x))
269 
+        flipped <- sample(nn, round(nn * properr))
270 
+        x[flipped] <- as.integer(!x[flipped])
271 
+        return(x)
272 
+    }
273 
+}
261 274
262 275 
 samplePop <- function(x, timeSample = "last",
263 276 
                       typeSample = "whole",
264 277 
                       thresholdWhole = 0.5,
265 278 
                       geneNames = NULL,
266 
-                      popSizeSample = NULL) {
279 
+                      popSizeSample = NULL,
280 
+                      propError = 0) {
267 281 
     ## timeSample <- match.arg(timeSample)
268 282 
     gN <- geneNames
269 283
... ... 
@@ -301,6 +315,9 @@ samplePop <- function(x, timeSample = "last",
301 315 
         nrow(z), " subjects and ",
302 316 
             ncol(z), " genes.\n")
303 317
318 
+    if(propError > 0) {
319 
+        z <- add_noise(z, propError)
320 
+    }
304 321 
     if(!is.null(gN)) {
305 322 
         colnames(z) <- gN
306 323 
     } else {
... ... 
@@ -530,7 +547,8 @@ oncoSimulIndiv <- function(fp,
530 547 
     if(is_null_na(detectionDrivers)) detectionDrivers <- (2^31) - 1
531 548 
     if(is_null_na(detectionSize)) detectionSize <- Inf
532 549 
     if(is_null_na(finalTime)) finalTime <- Inf
533 
-
550 
+
551 
+    if(is_null_na(sampleEvery)) stop("sampleEvery cannot be NULL or NA")
534 552
535 553 
     if(!inherits(fp, "fitnessEffects")) {
536 554 
         if(any(unlist(lapply(list(fp,
... ... 
@@ -694,18 +712,24 @@ oncoSimulIndiv <- function(fp,
694 712 
 }
695 713
696 714 
 summary.oncosimul <- function(object, ...) {
715 
+    ## This should be present even in HittedWallTime and HittedMaxTries
716 
+    ## if those are not regarded as errors
717 
+    pbp <- ("pops.by.time" %in% names(object) )
697 718
698 
-    if(object$other$UnrecoverExcept) ## yes, when bailing out from
719 
+    if(object$other$UnrecoverExcept) { ## yes, when bailing out from
699 720 
                                      ## except. can have just minimal
700 721 
                                      ## content
701 722 
         return(NA)
702 
-    else if (object$HittedWallTime || object$HittedMaxTries)
723 
+    } else if( !pbp & (object$HittedWallTime || object$HittedMaxTries) ) {
703 724 
         return(NA)
704 
-    else {
725 
+    } else if ( !pbp & !(object$HittedWallTime || object$HittedMaxTries) ) {
726 
+        stop("Eh? No pops.by.time but did not hit wall time or max tries? BUG!")
727 
+    } else {
705 728 
         tmp <- object[c("NumClones", "TotalPopSize", "LargestClone",
706 729 
                         "MaxNumDrivers", "MaxDriversLast",
707 730 
                         "NumDriversLargestPop", "TotalPresentDrivers",
708 
-                        "FinalTime", "NumIter", "HittedWallTime")]
731 
+                        "FinalTime", "NumIter", "HittedWallTime",
732 
+                        "HittedMaxTries")]
709 733
710 734 
         tmp$errorMF <- object$other$errorMF
711 735 
         tmp$minDMratio <- object$other$minDMratio
... ... 
@@ -1458,7 +1482,8 @@ phylogClone <- function(x, N = 1, t = "last", keepEvents = TRUE) {
1458 1482
1459 1483 
     df <- x$other$PhylogDF
1460 1484 
     if(nrow(df) == 0) {
1461 
-        warning("PhylogDF has 0 rows: no descendants of initMutant ever appeared.")
1485 
+        warning("PhylogDF has 0 rows: no descendants of initMutant ever appeared. ",
1486 
+                "This also happens if you did not set 'keepPhylog = TRUE'.")
1462 1487 
         return(NA)
1463 1488 
     }
1464 1489 
     if(!keepEvents) { ## is this just a graphical thing? or not?
R/generate-random-trees.R
History View file @ fc38a87
... ... 
@@ -15,10 +15,22 @@
15 15
16 16
17 17
18 
-simOGraph <- function(n, h = 4, conjunction = TRUE, nparents = 3,
18 
+simOGraph <- function(n, h = ifelse(n >= 4, 4, n),
19 
+                      conjunction = TRUE, nparents = 3,
19 20 
                       multilevelParent = TRUE,
20 21 
                       removeDirectIndirect = TRUE,
21 
-                      rootName = "Root") {
22 
+                      rootName = "Root",
23 
+                      geneNames = seq.int(n),
24 
+                      out = c("adjmat", "rT"),
25 
+                      s = 0.1,
26 
+                      sh = -0.1,
27 
+                      typeDep = "AND") {
28 
+    out <- match.arg(out)
29 
+    if(!is_null_na(geneNames)) {
30 
+        stopifnot(length(geneNames) == n)
31 
+    } else {
32 
+        geneNames <- 1:n
33 
+    }
22 34 
     ## Returns an adjacency matrix
23 35 
     if(h > n)
24 36 
         stop("h > n")
... ... 
@@ -65,13 +77,22 @@ simOGraph <- function(n, h = 4, conjunction = TRUE, nparents = 3,
65 77 
         adjMat[1, 2:(n+1) ] <- 1L
66 78 
     }
67 79
68 
-    colnames(adjMat) <- rownames(adjMat) <- c(rootName, 1:n)
69 
-
80 
+    colnames(adjMat) <- rownames(adjMat) <- c(rootName, geneNames)
81 
+
70 82
71 83 
     ## Prune to remove indirect connections
72 84 
     if(multilevelParent & removeDirectIndirect)
73 85 
         adjMat <- removeIndirectConnections(adjMat)
74 
-    return(adjMat)
86 
+    if(out == "adjmat")
87 
+        return(adjMat)
88 
+    else {
89 
+        df <- igraph::as_data_frame(igraph::graph.adjacency(adjMat))
90 
+        colnames(df) <- c("parent", "child")
91 
+        df$s <- s
92 
+        df$sh <- sh
93 
+        df$typeDep <- typeDep
94 
+        return(df)
95 
+    }
75 96 
 }
76 97
77 98 
 ## simOG <- simOGraph
R/new-restrict.R
History View file @ fc38a87
... ... 
@@ -1873,9 +1873,16 @@ sampledGenotypes <- function(y, genes = NULL) {
1873 1873 
     gn <- as.character(df[, 1])
1874 1874 
     gn[gn == ""] <- "WT"
1875 1875 
     df <- data.frame(Genotype = gn, Freq = df[, 2], stringsAsFactors = FALSE)
1876 
+    attributes(df)$ShannonI <- shannonI(df$Freq)
1877 
+    class(df) <- c("sampledGenotypes", class(df))
1876 1878 
     return(df)
1877 1879 
 }
1878 1880
1881 
+print.sampledGenotypes <- function(x, ...) {
1882 
+    print.data.frame(x, ...)
1883 
+    cat("\n Shannon's diversity (entropy) of sampled genotypes: ")
1884 
+    cat(attributes(x)$ShannonI, "\n")
1885 
+}
1879 1886
1880 1887
1881 1888 
 list_g_matches_fixed <- function(x, y) {
data/benchmark_1.RData
History View file @ fc38a87
1882 1889 
new file mode 100644
1883 1890 
Binary files /dev/null and b/data/benchmark_1.RData differ
data/benchmark_1_0.05.RData
History View file @ fc38a87
1884 1891 
new file mode 100644
1885 1892 
Binary files /dev/null and b/data/benchmark_1_0.05.RData differ
data/benchmark_2.RData
History View file @ fc38a87
1886 1893 
new file mode 100644
1887 1894 
Binary files /dev/null and b/data/benchmark_2.RData differ
data/benchmark_3.RData
History View file @ fc38a87
1888 1895 
new file mode 100644
1889 1896 
Binary files /dev/null and b/data/benchmark_3.RData differ
inst/NEWS
History View file @ fc38a87
... ... 
@@ -1,3 +1,12 @@
1 
+Changes in version 2.5.2 (2016-12-10):
2 
+        - Lots and lots of addition to vignette including benchmarks.
3 
+        - Diversity of sampled genotypes.
4 
+	- Genotyping error can be added in samplePop.
5 
+	- LOD and POM (lines of descent, path of maximum, sensu Szendro et
6 
+	  al.).
7 
+	- simOGraph can also out rT data frames.
8 
+	- Better (and better explained) estimates of simulation error for McFL.
9 
+
1 10 
 Changes in version 2.5.1 (2016-11-12):
2 11 
 	- AND of detectedSizeP and lastMaxDr.
3 12 
 	- fixation as stopping mechanism.
inst/miscell/benchmark_1.R
History View file @ fc38a87
4 13 
new file mode 100644
... ... 
@@ -0,0 +1,475 @@
1 
+### Benchmark runs. This produces "benchmark_1.RData"
2 
+
3 
+
4 
+rm(list = ls())
5 
+set.seed(NULL)
6 
+
7 
+library(OncoSimulR)
8 
+
9 
+######################################################################
10 
+######################################################################
11 
+######################################################################
12 
+######################################################################
13 
+
14 
+
15 
+system_summary <- function() {
16 
+    return(list(versioninfo = version,
17 
+                memimfo = system("free", intern = TRUE),
18 
+                cpuinfo = system("cat /proc/cpuinfo | grep 'model name'", intern = TRUE),
19 
+                packageinfo = paste("OncoSimulR, ", packageVersion("OncoSimulR")),
20 
+##                nodeinfo = Sys.info()$nodename,
21 
+##                nodelinuxinfo = paste(Sys.info()$sysname, Sys.info()$release),
22 
+                dateinfo = date()))
23 
+}
24 
+
25 
+stats_simuls <- function(sim) {
26 
+    ## sim is an oncoSimulPop output
27 
+    tmp <- do.call("rbind",
28 
+                   lapply(sim, function(x) c(NumClones = x$NumClones,
29 
+                                      NumIter = x$NumIter,
30 
+                                      FinalTime = x$FinalTime,
31 
+                                      TotalPopSize = x$TotalPopSize,
32 
+                                      Attempts = x$other$attemptsUsed)))
33 
+    unlist(lapply(as.data.frame(tmp), summary))
34 
+}
35 
+
36 
+all_sim_stats <- function(...) {
37 
+    ## Returns a single data frame with all benchmark info
38 
+    ## in ... pass the names of the objects
39 
+    names <- as.character(as.list(match.call())[-c(1)])
40 
+    m1 <- do.call("rbind", lapply(list(...), stats_simuls))
41 
+    rownames(m1) <- names
42 
+    Numindiv <- unlist(lapply(list(...), length))
43 
+    time <- unlist(lapply(paste0("t_", names), get))
44 
+    size <- unlist(lapply(list(...), object.size))
45 
+    df <- data.frame(m1, size, time, Numindiv)
46 
+    df$time_per_simul <- df$time/df$Numindiv
47 
+    df$size_mb_per_simul <- df$size/(df$Numindiv * 1024^2)
48 
+    attributes(df)$system_summary <- system_summary()
49 
+    return(df)
50 
+}
51 
+
52 
+######################################################################
53 
+######################################################################
54 
+######################################################################
55 
+######################################################################
56 
+
57 
+
58 
+pancr <- allFitnessEffects(
59 
+    data.frame(parent = c("Root", rep("KRAS", 4),
60 
+                   "SMAD4", "CDNK2A",
61 
+                   "TP53", "TP53", "MLL3"),
62 
+               child = c("KRAS","SMAD4", "CDNK2A",
63 
+                   "TP53", "MLL3",
64 
+                   rep("PXDN", 3), rep("TGFBR2", 2)),
65 
+               s = 0.1,
66 
+               sh = -0.9,
67 
+               typeDep = "MN"),
68 
+    drvNames = c("KRAS", "SMAD4", "CDNK2A", "TP53",
69 
+	             "MLL3", "TGFBR2", "PXDN"))
70 
+
71 
+
72 
+Nindiv <- 100
73 
+
74 
+
75 
+## keepEvery = 1
76 
+t_exp1 <- system.time(
77 
+    exp1 <- oncoSimulPop(Nindiv, pancr,
78 
+                            detectionProb = "default",
79 
+                            detectionSize = NA,
80 
+                            detectionDrivers = NA,
81 
+                            finalTime = NA,
82 
+                            keepPhylog = TRUE, keepEvery = 1,
83 
+                            model = "Exp",
84 
+                            mc.cores = 1))["elapsed"]
85 
+
86 
+
87 
+t_mc1 <- system.time(
88 
+    mc1 <- oncoSimulPop(Nindiv, pancr,
89 
+                           detectionProb = "default",
90 
+                           detectionSize = NA,
91 
+                           detectionDrivers = NA,
92 
+                           finalTime = NA,
93 
+                           keepPhylog = TRUE, keepEvery = 1,
94 
+                           model = "McFL",
95 
+                           mc.cores = 1))["elapsed"]
96 
+
97 
+## keepEvery = NA
98 
+t_exp2 <- system.time(
99 
+    exp2 <- oncoSimulPop(Nindiv, pancr,
100 
+                            detectionProb = "default",
101 
+                            detectionSize = NA,
102 
+                            detectionDrivers = NA,
103 
+                            finalTime = NA,
104 
+                            keepPhylog = TRUE, keepEvery = NA,
105 
+                            model = "Exp",
106 
+                            mc.cores = 1))["elapsed"]
107 
+
108 
+
109 
+t_mc2 <- system.time(
110 
+    mc2 <- oncoSimulPop(Nindiv, pancr,
111 
+                           detectionProb = "default",
112 
+                           detectionSize = NA,
113 
+                           detectionDrivers = NA,
114 
+                           finalTime = NA,
115 
+                           keepPhylog = TRUE, keepEvery = NA,
116 
+                           model = "McFL",
117 
+                           mc.cores = 1))["elapsed"]
118 
+
119 
+
120 
+
121 
+### exp3 to exp6
122 
+t_exp3 <- system.time(
123 
+    exp3 <- oncoSimulPop(Nindiv, pancr,
124 
+                            detectionProb = c(PDBaseline = 5e4,
125 
+                                              p2 = 0.1, n2 = 5e5,
126 
+                                              checkSizePEvery = 20),
127 
+                            detectionSize = NA,
128 
+                            detectionDrivers = NA,
129 
+                            finalTime = NA,
130 
+                            keepPhylog = TRUE, keepEvery = 1,
131 
+                            model = "Exp",
132 
+                            mc.cores = 1))["elapsed"]
133 
+
134 
+t_exp4 <- system.time(
135 
+    exp4 <- oncoSimulPop(Nindiv, pancr,
136 
+                            detectionProb = c(PDBaseline = 5e4,
137 
+                                              p2 = 0.1, n2 = 5e5,
138 
+                                              checkSizePEvery = 20),
139 
+                            detectionSize = NA,
140 
+                            detectionDrivers = NA,
141 
+                            finalTime = NA,
142 
+                            keepPhylog = TRUE, keepEvery = NA,
143 
+                            model = "Exp",
144 
+                            mc.cores = 1))["elapsed"]
145 
+
146 
+
147 
+
148 
+t_exp5 <- system.time(
149 
+    exp5 <- oncoSimulPop(Nindiv, pancr,
150 
+                            detectionProb = c(PDBaseline = 5e5,
151 
+                                              p2 = 0.1, n2 = 5e7,
152 
+                                              checkSizePEvery = 20),
153 
+                            detectionSize = NA,
154 
+                            detectionDrivers = NA,
155 
+                            finalTime = NA,
156 
+                            keepPhylog = TRUE, keepEvery = 1,
157 
+                            model = "Exp",
158 
+                            mc.cores = 1))["elapsed"]
159 
+
160 
+t_exp6 <- system.time(
161 
+    exp6 <- oncoSimulPop(Nindiv, pancr,
162 
+                            detectionProb = c(PDBaseline = 5e5,
163 
+                                              p2 = 0.1, n2 = 5e7,
164 
+                                              checkSizePEvery = 20),
165 
+                            detectionSize = NA,
166 
+                            detectionDrivers = NA,
167 
+                            finalTime = NA,
168 
+                            keepPhylog = TRUE, keepEvery = NA,
169 
+                            model = "Exp",
170 
+                            mc.cores = 1))["elapsed"]
171 
+
172 
+
173 
+####################################################
174 
+###### onlyCancer = FALSE
175 
+
176 
+
177 
+## keepEvery = 1
178 
+t_exp1_noc <- system.time(
179 
+    exp1_noc <- oncoSimulPop(Nindiv, pancr,
180 
+                            detectionProb = "default",
181 
+                            detectionSize = NA,
182 
+                            detectionDrivers = NA,
183 
+                            finalTime = NA, onlyCancer = FALSE,
184 
+                            keepPhylog = TRUE, keepEvery = 1,
185 
+                            model = "Exp",
186 
+                            mc.cores = 1))["elapsed"]
187 
+t_mc1_noc <- system.time(
188 
+    mc1_noc <- oncoSimulPop(Nindiv, pancr,
189 
+                           detectionProb = "default",
190 
+                           detectionSize = NA,
191 
+                           detectionDrivers = NA,
192 
+                           finalTime = NA, onlyCancer = FALSE,
193 
+                           keepPhylog = TRUE, keepEvery = 1,
194 
+                           model = "McFL",
195 
+                           mc.cores = 1))["elapsed"]
196 
+## keepEvery = NA
197 
+t_exp2_noc <- system.time(
198 
+    exp2_noc <- oncoSimulPop(Nindiv, pancr,
199 
+                            detectionProb = "default",
200 
+                            detectionSize = NA,
201 
+                            detectionDrivers = NA,
202 
+                            finalTime = NA, onlyCancer = FALSE,
203 
+                            keepPhylog = TRUE, keepEvery = NA,
204 
+                            model = "Exp",
205 
+                            mc.cores = 1))["elapsed"]
206 
+t_mc2_noc <- system.time(
207 
+    mc2_noc <- oncoSimulPop(Nindiv, pancr,
208 
+                           detectionProb = "default",
209 
+                           detectionSize = NA,
210 
+                           detectionDrivers = NA,
211 
+                           finalTime = NA, onlyCancer = FALSE,
212 
+                           keepPhylog = TRUE, keepEvery = NA,
213 
+                           model = "McFL",
214 
+                           mc.cores = 1))["elapsed"]
215 
+### exp3_noc to exp6_noc
216 
+t_exp3_noc <- system.time(
217 
+    exp3_noc <- oncoSimulPop(Nindiv, pancr,
218 
+                            detectionProb = c(PDBaseline = 5e4,
219 
+                                              p2 = 0.1, n2 = 5e5,
220 
+                                              checkSizePEvery = 20),
221 
+                            detectionSize = NA,
222 
+                            detectionDrivers = NA,
223 
+                            finalTime = NA, onlyCancer = FALSE,
224 
+                            keepPhylog = TRUE, keepEvery = 1,
225 
+                            model = "Exp",
226 
+                            mc.cores = 1))["elapsed"]
227 
+t_exp4_noc <- system.time(
228 
+    exp4_noc <- oncoSimulPop(Nindiv, pancr,
229 
+                            detectionProb = c(PDBaseline = 5e4,
230 
+                                              p2 = 0.1, n2 = 5e5,
231 
+                                              checkSizePEvery = 20),
232 
+                            detectionSize = NA,
233 
+                            detectionDrivers = NA,
234 
+                            finalTime = NA, onlyCancer = FALSE,
235 
+                            keepPhylog = TRUE, keepEvery = NA,
236 
+                            model = "Exp",
237 
+                            mc.cores = 1))["elapsed"]
238 
+t_exp5_noc <- system.time(
239 
+    exp5_noc <- oncoSimulPop(Nindiv, pancr,
240 
+                            detectionProb = c(PDBaseline = 5e5,
241 
+                                              p2 = 0.1, n2 = 5e7,
242 
+                                              checkSizePEvery = 20),
243 
+                            detectionSize = NA,
244 
+                            detectionDrivers = NA,
245 
+                            finalTime = NA, onlyCancer = FALSE,
246 
+                            keepPhylog = TRUE, keepEvery = 1,
247 
+                            model = "Exp",
248 
+                            mc.cores = 1))["elapsed"]
249 
+t_exp6_noc <- system.time(
250 
+    exp6_noc <- oncoSimulPop(Nindiv, pancr,
251 
+                            detectionProb = c(PDBaseline = 5e5,
252 
+                                              p2 = 0.1, n2 = 5e7,
253 
+                                              checkSizePEvery = 20),
254 
+                            detectionSize = NA,
255 
+                            detectionDrivers = NA,
256 
+                            finalTime = NA, onlyCancer = FALSE,
257 
+                            keepPhylog = TRUE, keepEvery = NA,
258 
+                            model = "Exp",
259 
+                            mc.cores = 1))["elapsed"]
260 
+
261 
+benchmark_1 <- all_sim_stats(exp1, mc1, exp2, mc2, exp3, exp4, exp5, exp6,
262 
+                         exp1_noc, mc1_noc, exp2_noc, mc2_noc, exp3_noc, exp4_noc, exp5_noc, exp6_noc
263 
+                         )
264 
+
265 
+## Add the info about key settings
266 
+benchmark_1$keepEvery <- c(1, 1, NA, NA, 1, NA, 1, NA,
267 
+                           1, 1, NA, NA, 1, NA, 1, NA)
268 
+benchmark_1$PDBaseline <- c(rep(1.2 * 500, 4), 5e4, 5e4, 5e5, 5e5,
269 
+                            rep(1.2 * 500, 4), 5e4, 5e4, 5e5, 5e5)
270 
+benchmark_1$n2 <- c(rep(2 * 500, 4), 5e5, 5e5, 5e7, 5e7,
271 
+                    rep(2 * 500, 4), 5e5, 5e5, 5e7, 5e7)
272 
+benchmark_1$onlyCancer <- c(rep(TRUE, 8), rep(FALSE, 8))
273 
+
274 
+save(file = "../../data/benchmark_1.RData", benchmark_1)
275 
+
276 
+
277 
+gc()
278 
+
279 
+## ######################################################################
280 
+## ######################################################################
281 
+## ###############                                         ##############
282 
+## ###############      If you do it by hand ...           ##############
283 
+## ###############                                         ##############
284 
+## ######################################################################
285 
+## ######################################################################
286 
+
287 
+
288 
+
289 
+## cat("\n\n\n t_exp1 = ", t_exp1, "\n")
290 
+## object.size(exp1)/(Nindiv * 1024^2)
291 
+## cat("\n\n")
292 
+## summary(unlist(lapply(exp1, "[[", "NumClones")))
293 
+## summary(unlist(lapply(exp1, "[[", "NumIter")))
294 
+## summary(unlist(lapply(exp1, "[[", "FinalTime")))
295 
+## summary(unlist(lapply(exp1, "[[", "TotalPopSize")))
296 
+
297 
+
298 
+## cat("\n\n")
299 
+## cat("\n\n\n t_mc1 = ", t_mc1, "\n")
300 
+## object.size(mc1)/(Nindiv * 1024^2)
301 
+## cat("\n\n")
302 
+## summary(unlist(lapply(mc1, "[[", "NumClones")))
303 
+## summary(unlist(lapply(mc1, "[[", "NumIter")))
304 
+## summary(unlist(lapply(mc1, "[[", "FinalTime")))
305 
+## summary(unlist(lapply(mc1, "[[", "TotalPopSize")))
306 
+
307 
+## cat("\n\n")
308 
+## cat("\n\n\n t_exp2 = ", t_exp2, "\n")
309 
+## object.size(exp2)/(Nindiv * 1024^2)
310 
+## cat("\n\n")
311 
+## summary(unlist(lapply(exp2, "[[", "NumClones")))
312 
+## summary(unlist(lapply(exp2, "[[", "NumIter")))
313 
+## summary(unlist(lapply(exp2, "[[", "FinalTime")))
314 
+## summary(unlist(lapply(exp2, "[[", "TotalPopSize")))
315 
+
316 
+## cat("\n\n")
317 
+## cat("\n\n\n t_mc2 = ", t_mc2, "\n")
318 
+## object.size(mc2)/(Nindiv * 1024^2)
319 
+## cat("\n\n")
320 
+## summary(unlist(lapply(mc2, "[[", "NumClones")))
321 
+## summary(unlist(lapply(mc2, "[[", "NumIter")))
322 
+## summary(unlist(lapply(mc2, "[[", "FinalTime")))
323 
+## summary(unlist(lapply(mc2, "[[", "TotalPopSize")))
324 
+
325 
+
326 
+
327 
+## cat("\n\n")
328 
+## cat("\n\n\n t_exp3 = ", t_exp3, "\n")
329 
+## object.size(exp3)/(Nindiv * 1024^2)
330 
+## cat("\n\n")
331 
+## summary(unlist(lapply(exp3, "[[", "NumClones")))
332 
+## summary(unlist(lapply(exp3, "[[", "NumIter")))
333 
+## summary(unlist(lapply(exp3, "[[", "FinalTime")))
334 
+## summary(unlist(lapply(exp3, "[[", "TotalPopSize")))
335 
+
336 
+
337 
+## cat("\n\n")
338 
+## cat("\n\n\n t_exp4 = ", t_exp4, "\n")
339 
+## object.size(exp4)/(Nindiv * 1024^2)
340 
+## cat("\n\n")
341 
+## summary(unlist(lapply(exp4, "[[", "NumClones")))
342 
+## summary(unlist(lapply(exp4, "[[", "NumIter")))
343 
+## summary(unlist(lapply(exp4, "[[", "FinalTime")))
344 
+## summary(unlist(lapply(exp4, "[[", "TotalPopSize")))
345 
+
346 
+
347 
+## cat("\n\n")
348 
+## cat("\n\n\n t_exp5 = ", t_exp5, "\n")
349 
+## object.size(exp5)/(Nindiv * 1024^2)
350 
+## cat("\n\n")
351 
+## summary(unlist(lapply(exp5, "[[", "NumClones")))
352 
+## summary(unlist(lapply(exp5, "[[", "NumIter")))
353 
+## summary(unlist(lapply(exp5, "[[", "FinalTime")))
354 
+## summary(unlist(lapply(exp5, "[[", "TotalPopSize")))
355 
+
356 
+
357 
+## cat("\n\n")
358 
+## cat("\n\n\n t_exp6 = ", t_exp6, "\n")
359 
+## object.size(exp6)/(Nindiv * 1024^2)
360 
+## cat("\n\n")
361 
+## summary(unlist(lapply(exp6, "[[", "NumClones")))
362 
+## summary(unlist(lapply(exp6, "[[", "NumIter")))
363 
+## summary(unlist(lapply(exp6, "[[", "FinalTime")))
364 
+## summary(unlist(lapply(exp6, "[[", "TotalPopSize")))
365 
+
366 
+
367 
+## ## Median runs until cancer
368 
+
369 
+## lapply(list(exp1, mc1, exp2, mc2, exp3, exp4, exp5, exp6),
370 
+##        function(y) median(unlist(lapply(y, function(x) x$other$attemptsUsed))))
371 
+
372 
+
373 
+
374 
+
375 
+
376 
+
377 
+## cat("\n\n\n t_exp1_noc = ", t_exp1_noc, "\n")
378 
+## object.size(exp1_noc)/(Nindiv * 1024^2)
379 
+## cat("\n\n")
380 
+## summary(unlist(lapply(exp1_noc, "[[", "NumClones")))
381 
+## summary(unlist(lapply(exp1_noc, "[[", "NumIter")))
382 
+## summary(unlist(lapply(exp1_noc, "[[", "FinalTime")))
383 
+## summary(unlist(lapply(exp1_noc, "[[", "TotalPopSize")))
384 
+
385 
+
386 
+## cat("\n\n")
387 
+## cat("\n\n\n t_mc1_noc = ", t_mc1_noc, "\n")
388 
+## object.size(mc1_noc)/(Nindiv * 1024^2)
389 
+## cat("\n\n")
390 
+## summary(unlist(lapply(mc1_noc, "[[", "NumClones")))
391 
+## summary(unlist(lapply(mc1_noc, "[[", "NumIter")))
392 
+## summary(unlist(lapply(mc1_noc, "[[", "FinalTime")))
393 
+## summary(unlist(lapply(mc1_noc, "[[", "TotalPopSize")))
394 
+
395 
+## cat("\n\n")
396 
+## cat("\n\n\n t_exp2_noc = ", t_exp2_noc, "\n")
397 
+## object.size(exp2_noc)/(Nindiv * 1024^2)
398 
+## cat("\n\n")
399 
+## summary(unlist(lapply(exp2_noc, "[[", "NumClones")))
400 
+## summary(unlist(lapply(exp2_noc, "[[", "NumIter")))
401 
+## summary(unlist(lapply(exp2_noc, "[[", "FinalTime")))
402 
+## summary(unlist(lapply(exp2_noc, "[[", "TotalPopSize")))
403 
+
404 
+## cat("\n\n")
405 
+## cat("\n\n\n t_mc2_noc = ", t_mc2_noc, "\n")
406 
+## object.size(mc2_noc)/(Nindiv * 1024^2)
407 
+## cat("\n\n")
408 
+## summary(unlist(lapply(mc2_noc, "[[", "NumClones")))
409 
+## summary(unlist(lapply(mc2_noc, "[[", "NumIter")))
410 
+## summary(unlist(lapply(mc2_noc, "[[", "FinalTime")))
411 
+## summary(unlist(lapply(mc2_noc, "[[", "TotalPopSize")))
412 
+
413 
+
414 
+
415 
+## cat("\n\n")
416 
+## cat("\n\n\n t_exp3_noc = ", t_exp3_noc, "\n")
417 
+## object.size(exp3_noc)/(Nindiv * 1024^2)
418 
+## cat("\n\n")
419 
+## summary(unlist(lapply(exp3_noc, "[[", "NumClones")))
420 
+## summary(unlist(lapply(exp3_noc, "[[", "NumIter")))
421 
+## summary(unlist(lapply(exp3_noc, "[[", "FinalTime")))
422 
+## summary(unlist(lapply(exp3_noc, "[[", "TotalPopSize")))
423 
+
424 
+
425 
+## cat("\n\n")
426 
+## cat("\n\n\n t_exp4_noc = ", t_exp4_noc, "\n")
427 
+## object.size(exp4_noc)/(Nindiv * 1024^2)
428 
+## cat("\n\n")
429 
+## summary(unlist(lapply(exp4_noc, "[[", "NumClones")))
430 
+## summary(unlist(lapply(exp4_noc, "[[", "NumIter")))
431 
+## summary(unlist(lapply(exp4_noc, "[[", "FinalTime")))
432 
+## summary(unlist(lapply(exp4_noc, "[[", "TotalPopSize")))
433 
+
434 
+
435 
+## cat("\n\n")
436 
+## cat("\n\n\n t_exp5_noc = ", t_exp5_noc, "\n")
437 
+## object.size(exp5_noc)/(Nindiv * 1024^2)
438 
+## cat("\n\n")
439 
+## summary(unlist(lapply(exp5_noc, "[[", "NumClones")))
440 
+## summary(unlist(lapply(exp5_noc, "[[", "NumIter")))
441 
+## summary(unlist(lapply(exp5_noc, "[[", "FinalTime")))
442 
+## summary(unlist(lapply(exp5_noc, "[[", "TotalPopSize")))
443 
+
444 
+
445 
+## cat("\n\n")
446 
+## cat("\n\n\n t_exp6_noc = ", t_exp6_noc, "\n")
447 
+## object.size(exp6_noc)/(Nindiv * 1024^2)
448 
+## cat("\n\n")
449 
+## summary(unlist(lapply(exp6_noc, "[[", "NumClones")))
450 
+## summary(unlist(lapply(exp6_noc, "[[", "NumIter")))
451 
+## summary(unlist(lapply(exp6_noc, "[[", "FinalTime")))
452 
+## summary(unlist(lapply(exp6_noc, "[[", "TotalPopSize")))
453 
+
454 
+
455 
+## ## Median runs until cancer
456 
+
457 
+## lapply(list(exp1_noc, mc1_noc, exp2_noc, mc2_noc, exp3_noc, exp4_noc, exp5_noc, exp6_noc),
458 
+##        function(y) median(unlist(lapply(y, function(x) x$other$attemptsUsed))))
459 
+
460 
+
461 
+
462 
+
463 
+
464 
+## ## bench1 <- data.frame(
465 
+## ##     time = c(t_exp1, t_mc1, t_exp2, t_mc2, t_exp3, t_exp4, t_exp5, t_exp6),
466 
+## ##     size = unlist(lapply(list(exp1, mc1, exp2, mc2, exp3, exp4, exp5, exp6),
467 
+## ##                          object.size))/(Nindiv * 1024^2),
468 
+
469 
+## ##     lapply(list(exp1, mc1, exp2, mc2, exp3, exp4, exp5, exp6),
470 
+## ##            function(y) median(unlist(lapply(y, function(x) x$other$attemptsUsed))))
471 
+## ## )
472 
+
473 
+
474 
+
475 
+
inst/miscell/benchmark_1_0.05.R
History View file @ fc38a87
0 476 
new file mode 100644
... ... 
@@ -0,0 +1,484 @@
1 
+### Benchmark runs. This produces "benchmark_1.RData"
2 
+
3 
+
4 
+rm(list = ls())
5 
+set.seed(NULL)
6 
+
7 
+library(OncoSimulR)
8 
+
9 
+######################################################################
10 
+######################################################################
11 
+######################################################################
12 
+######################################################################
13 
+
14 
+
15 
+system_summary <- function() {
16 
+    return(list(versioninfo = version,
17 
+                memimfo = system("free", intern = TRUE),
18 
+                cpuinfo = system("cat /proc/cpuinfo | grep 'model name'", intern = TRUE),
19 
+                packageinfo = paste("OncoSimulR, ", packageVersion("OncoSimulR")),
20 
+##                nodeinfo = Sys.info()$nodename,
21 
+##                nodelinuxinfo = paste(Sys.info()$sysname, Sys.info()$release),
22 
+                dateinfo = date()))
23 
+}
24 
+stats_simuls <- function(sim) {
25 
+    ## sim is an oncoSimulPop output
26 
+    trf <- function(x) {
27 
+        tt <- try(c(NumClones = x$NumClones,
28 
+                    NumIter = x$NumIter,
29 
+                    FinalTime = x$FinalTime,
30 
+                    TotalPopSize = x$TotalPopSize,
31 
+                    Attempts = x$other$attemptsUsed))
32 
+        if(!inherits(tt, "try-error")) {
33 
+            return(tt)
34 
+        } else {
35 
+            return(c(NumClones = NA,
36 
+                    NumIter = NA,
37 
+                    FinalTime = NA,
38 
+                    TotalPopSize = NA,
39 
+                    Attempts = NA))
40 
+        }
41 
+    }
42 
+    tmp <- try(do.call("rbind", lapply(sim, trf)))
43 
+    unlist(lapply(as.data.frame(tmp), summary))
44 
+}
45 
+
46 
+all_sim_stats <- function(...) {
47 
+    ## Returns a single data frame with all benchmark info
48 
+    ## in ... pass the names of the objects
49 
+    names <- as.character(as.list(match.call())[-c(1)])
50 
+    m1 <- do.call("rbind", lapply(list(...), stats_simuls))
51 
+    rownames(m1) <- names
52 
+    Numindiv <- unlist(lapply(list(...), length))
53 
+    time <- unlist(lapply(paste0("t_", names), get))
54 
+    size <- unlist(lapply(list(...), object.size))
55 
+    df <- data.frame(m1, size, time, Numindiv)
56 
+    df$time_per_simul <- df$time/df$Numindiv
57 
+    df$size_mb_per_simul <- df$size/(df$Numindiv * 1024^2)
58 
+    attributes(df)$system_summary <- system_summary()
59 
+    return(df)
60 
+}
61 
+
62 
+######################################################################
63 
+######################################################################
64 
+######################################################################
65 
+######################################################################
66 
+
67 
+
68 
+pancr <- allFitnessEffects(
69 
+    data.frame(parent = c("Root", rep("KRAS", 4),
70 
+                   "SMAD4", "CDNK2A",
71 
+                   "TP53", "TP53", "MLL3"),
72 
+               child = c("KRAS","SMAD4", "CDNK2A",
73 
+                   "TP53", "MLL3",
74 
+                   rep("PXDN", 3), rep("TGFBR2", 2)),
75 
+               s = 0.05,
76 
+               sh = -0.9,
77 
+               typeDep = "MN"),
78 
+    drvNames = c("KRAS", "SMAD4", "CDNK2A", "TP53",
79 
+	             "MLL3", "TGFBR2", "PXDN"))
80 
+
81 
+
82 
+Nindiv <- 100
83 
+
84 
+
85 
+## keepEvery = 1
86 
+t_exp1 <- system.time(
87 
+    exp1 <- oncoSimulPop(Nindiv, pancr,
88 
+                            detectionProb = "default",
89 
+                            detectionSize = NA,
90 
+                            detectionDrivers = NA,
91 
+                            finalTime = NA,
92 
+                            keepPhylog = TRUE, keepEvery = 1,
93 
+                            model = "Exp",
94 
+                            mc.cores = 1))["elapsed"]
95 
+
96 
+
97 
+t_mc1 <- system.time(
98 
+    mc1 <- oncoSimulPop(Nindiv, pancr,
99 
+                           detectionProb = "default",
100 
+                           detectionSize = NA,
101 
+                           detectionDrivers = NA,
102 
+                           finalTime = NA,
103 
+                           keepPhylog = TRUE, keepEvery = 1,
104 
+                           model = "McFL",
105 
+                           mc.cores = 1))["elapsed"]
106 
+
107 
+## keepEvery = NA
108 
+t_exp2 <- system.time(
109 
+    exp2 <- oncoSimulPop(Nindiv, pancr,
110 
+                            detectionProb = "default",
111 
+                            detectionSize = NA,
112 
+                            detectionDrivers = NA,
113 
+                            finalTime = NA,
114 
+                            keepPhylog = TRUE, keepEvery = NA,
115 
+                            model = "Exp",
116 
+                            mc.cores = 1))["elapsed"]
117 
+
118 
+
119 
+t_mc2 <- system.time(
120 
+    mc2 <- oncoSimulPop(Nindiv, pancr,
121 
+                           detectionProb = "default",
122 
+                           detectionSize = NA,
123 
+                           detectionDrivers = NA,
124 
+                           finalTime = NA,
125 
+                           keepPhylog = TRUE, keepEvery = NA,
126 
+                           model = "McFL",
127 
+                           mc.cores = 1))["elapsed"]
128 
+
129 
+
130 
+
131 
+### exp3 to exp6
132 
+t_exp3 <- system.time(
133 
+    exp3 <- oncoSimulPop(Nindiv, pancr,
134 
+                            detectionProb = c(PDBaseline = 5e4,
135 
+                                              p2 = 0.1, n2 = 5e5,
136 
+                                              checkSizePEvery = 20),
137 
+                            detectionSize = NA,
138 
+                            detectionDrivers = NA,
139 
+                            finalTime = NA,
140 
+                            keepPhylog = TRUE, keepEvery = 1,
141 
+                            model = "Exp",
142 
+                            mc.cores = 1))["elapsed"]
143 
+
144 
+t_exp4 <- system.time(
145 
+    exp4 <- oncoSimulPop(Nindiv, pancr,
146 
+                            detectionProb = c(PDBaseline = 5e4,
147 
+                                              p2 = 0.1, n2 = 5e5,
148 
+                                              checkSizePEvery = 20),
149 
+                            detectionSize = NA,
150 
+                            detectionDrivers = NA,
151 
+                            finalTime = NA,
152 
+                            keepPhylog = TRUE, keepEvery = NA,
153 
+                            model = "Exp",
154 
+                            mc.cores = 1))["elapsed"]
155 
+
156 
+
157 
+
158 
+t_exp5 <- system.time(
159 
+    exp5 <- oncoSimulPop(Nindiv, pancr,
160 
+                            detectionProb = c(PDBaseline = 5e5,
161 
+                                              p2 = 0.1, n2 = 5e7,
162 
+                                              checkSizePEvery = 20),
163 
+                            detectionSize = NA,
164 
+                            detectionDrivers = NA,
165 
+                            finalTime = NA,
166 
+                            keepPhylog = TRUE, keepEvery = 1,
167 
+                            model = "Exp",
168 
+                            mc.cores = 1))["elapsed"]
169 
+
170 
+t_exp6 <- system.time(
171 
+    exp6 <- oncoSimulPop(Nindiv, pancr,
172 
+                            detectionProb = c(PDBaseline = 5e5,
173 
+                                              p2 = 0.1, n2 = 5e7,
174 
+                                              checkSizePEvery = 20),
175 
+                            detectionSize = NA,
176 
+                            detectionDrivers = NA,
177 
+                            finalTime = NA,
178 
+                            keepPhylog = TRUE, keepEvery = NA,
179 
+                            model = "Exp",
180 
+                            mc.cores = 1))["elapsed"]
181 
+
182 
+
183 
+####################################################
184 
+###### onlyCancer = FALSE
185 
+
186 
+
187 
+## keepEvery = 1
188 
+t_exp1_noc <- system.time(
189 
+    exp1_noc <- oncoSimulPop(Nindiv, pancr,
190 
+                            detectionProb = "default",
191 
+                            detectionSize = NA,
192 
+                            detectionDrivers = NA,
193 
+                            finalTime = NA, onlyCancer = FALSE,
194 
+                            keepPhylog = TRUE, keepEvery = 1,
195 
+                            model = "Exp",
196 
+                            mc.cores = 1))["elapsed"]
197 
+t_mc1_noc <- system.time(
198 
+    mc1_noc <- oncoSimulPop(Nindiv, pancr,
199 
+                           detectionProb = "default",
200 
+                           detectionSize = NA,
201 
+                           detectionDrivers = NA,
202 
+                           finalTime = NA, onlyCancer = FALSE,
203 
+                           keepPhylog = TRUE, keepEvery = 1,
204 
+                           model = "McFL",
205 
+                           mc.cores = 1))["elapsed"]
206 
+## keepEvery = NA
207 
+t_exp2_noc <- system.time(
208 
+    exp2_noc <- oncoSimulPop(Nindiv, pancr,
209 
+                            detectionProb = "default",
210 
+                            detectionSize = NA,
211 
+                            detectionDrivers = NA,
212 
+                            finalTime = NA, onlyCancer = FALSE,
213 
+                            keepPhylog = TRUE, keepEvery = NA,
214 
+                            model = "Exp",
215 
+                            mc.cores = 1))["elapsed"]
216 
+t_mc2_noc <- system.time(
217 
+    mc2_noc <- oncoSimulPop(Nindiv, pancr,
218 
+                           detectionProb = "default",
219 
+                           detectionSize = NA,
220 
+                           detectionDrivers = NA,
221 
+                           finalTime = NA, onlyCancer = FALSE,
222 
+                           keepPhylog = TRUE, keepEvery = NA,
223 
+                           model = "McFL",
224 
+                           mc.cores = 1))["elapsed"]
225 
+### exp3_noc to exp6_noc
226 
+t_exp3_noc <- system.time(
227 
+    exp3_noc <- oncoSimulPop(Nindiv, pancr,
228 
+                            detectionProb = c(PDBaseline = 5e4,
229 
+                                              p2 = 0.1, n2 = 5e5,
230 
+                                              checkSizePEvery = 20),
231 
+                            detectionSize = NA,
232 
+                            detectionDrivers = NA,
233 
+                            finalTime = NA, onlyCancer = FALSE,
234 
+                            keepPhylog = TRUE, keepEvery = 1,
235 
+                            model = "Exp",
236 
+                            mc.cores = 1))["elapsed"]
237 
+t_exp4_noc <- system.time(
238 
+    exp4_noc <- oncoSimulPop(Nindiv, pancr,
239 
+                            detectionProb = c(PDBaseline = 5e4,
240 
+                                              p2 = 0.1, n2 = 5e5,
241 
+                                              checkSizePEvery = 20),
242 
+                            detectionSize = NA,
243 
+                            detectionDrivers = NA,
244 
+                            finalTime = NA, onlyCancer = FALSE,
245 
+                            keepPhylog = TRUE, keepEvery = NA,
246 
+                            model = "Exp",
247 
+                            mc.cores = 1))["elapsed"]
248 
+t_exp5_noc <- system.time(
249 
+    exp5_noc <- oncoSimulPop(Nindiv, pancr,
250 
+                            detectionProb = c(PDBaseline = 5e5,
251 
+                                              p2 = 0.1, n2 = 5e7,
252 
+                                              checkSizePEvery = 20),
253 
+                            detectionSize = NA,
254 
+                            detectionDrivers = NA,
255 
+                            finalTime = NA, onlyCancer = FALSE,
256 
+                            keepPhylog = TRUE, keepEvery = 1,
257 
+                            model = "Exp",
258 
+                            mc.cores = 1))["elapsed"]
259 
+t_exp6_noc <- system.time(
260 
+    exp6_noc <- oncoSimulPop(Nindiv, pancr,
261 
+                            detectionProb = c(PDBaseline = 5e5,
262 
+                                              p2 = 0.1, n2 = 5e7,
263 
+                                              checkSizePEvery = 20),
264 
+                            detectionSize = NA,
265 
+                            detectionDrivers = NA,
266 
+                            finalTime = NA, onlyCancer = FALSE,
267 
+                            keepPhylog = TRUE, keepEvery = NA,
268 
+                            model = "Exp",
269 
+                            mc.cores = 1))["elapsed"]
270 
+
271 
+benchmark_1_0.05 <- all_sim_stats(exp1, mc1, exp2, mc2, exp3, exp4, exp5, exp6,
272 
+                         exp1_noc, mc1_noc, exp2_noc, mc2_noc, exp3_noc, exp4_noc, exp5_noc, exp6_noc
273 
+                         )
274 
+
275 
+## Add the info about key settings
276 
+benchmark_1_0.05$keepEvery <- c(1, 1, NA, NA, 1, NA, 1, NA,
277 
+                           1, 1, NA, NA, 1, NA, 1, NA)
278 
+benchmark_1_0.05$PDBaseline <- c(rep(1.2 * 500, 4), 5e4, 5e4, 5e5, 5e5,
279 
+                            rep(1.2 * 500, 4), 5e4, 5e4, 5e5, 5e5)
280 
+benchmark_1_0.05$n2 <- c(rep(2 * 500, 4), 5e5, 5e5, 5e7, 5e7,
281 
+                    rep(2 * 500, 4), 5e5, 5e5, 5e7, 5e7)
282 
+benchmark_1_0.05$onlyCancer <- c(rep(TRUE, 8), rep(FALSE, 8))
283 
+save(file = "../../data/benchmark_1_0.05.RData", benchmark_1_0.05)
284 
+
285 
+
286 
+gc()
287 
+
288 
+## ######################################################################
289 
+## ######################################################################
290 
+## ###############                                         ##############
291 
+## ###############      If you do it by hand ...           ##############
292 
+## ###############                                         ##############
293 
+## ######################################################################
294 
+## ######################################################################
295 
+
296 
+
297 
+
298 
+## cat("\n\n\n t_exp1 = ", t_exp1, "\n")
299 
+## object.size(exp1)/(Nindiv * 1024^2)
300 
+## cat("\n\n")
301 
+## summary(unlist(lapply(exp1, "[[", "NumClones")))
302 
+## summary(unlist(lapply(exp1, "[[", "NumIter")))
303 
+## summary(unlist(lapply(exp1, "[[", "FinalTime")))
304 
+## summary(unlist(lapply(exp1, "[[", "TotalPopSize")))
305 
+
306 
+
307 
+## cat("\n\n")
308 
+## cat("\n\n\n t_mc1 = ", t_mc1, "\n")
309 
+## object.size(mc1)/(Nindiv * 1024^2)
310 
+## cat("\n\n")
311 
+## summary(unlist(lapply(mc1, "[[", "NumClones")))
312 
+## summary(unlist(lapply(mc1, "[[", "NumIter")))
313 
+## summary(unlist(lapply(mc1, "[[", "FinalTime")))
314 
+## summary(unlist(lapply(mc1, "[[", "TotalPopSize")))
315 
+
316 
+## cat("\n\n")
317 
+## cat("\n\n\n t_exp2 = ", t_exp2, "\n")
318 
+## object.size(exp2)/(Nindiv * 1024^2)
319 
+## cat("\n\n")
320 
+## summary(unlist(lapply(exp2, "[[", "NumClones")))
321 
+## summary(unlist(lapply(exp2, "[[", "NumIter")))
322 
+## summary(unlist(lapply(exp2, "[[", "FinalTime")))
323 
+## summary(unlist(lapply(exp2, "[[", "TotalPopSize")))
324 
+
325 
+## cat("\n\n")
326 
+## cat("\n\n\n t_mc2 = ", t_mc2, "\n")
327 
+## object.size(mc2)/(Nindiv * 1024^2)
328 
+## cat("\n\n")
329 
+## summary(unlist(lapply(mc2, "[[", "NumClones")))
330 
+## summary(unlist(lapply(mc2, "[[", "NumIter")))
331 
+## summary(unlist(lapply(mc2, "[[", "FinalTime")))
332 
+## summary(unlist(lapply(mc2, "[[", "TotalPopSize")))
333 
+
334 
+
335 
+
336 
+## cat("\n\n")
337 
+## cat("\n\n\n t_exp3 = ", t_exp3, "\n")
338 
+## object.size(exp3)/(Nindiv * 1024^2)
339 
+## cat("\n\n")
340 
+## summary(unlist(lapply(exp3, "[[", "NumClones")))
341 
+## summary(unlist(lapply(exp3, "[[", "NumIter")))
342 
+## summary(unlist(lapply(exp3, "[[", "FinalTime")))
343 
+## summary(unlist(lapply(exp3, "[[", "TotalPopSize")))
344 
+
345 
+
346 
+## cat("\n\n")
347 
+## cat("\n\n\n t_exp4 = ", t_exp4, "\n")
348 
+## object.size(exp4)/(Nindiv * 1024^2)
349 
+## cat("\n\n")
350 
+## summary(unlist(lapply(exp4, "[[", "NumClones")))
351 
+## summary(unlist(lapply(exp4, "[[", "NumIter")))
352 
+## summary(unlist(lapply(exp4, "[[", "FinalTime")))
353 
+## summary(unlist(lapply(exp4, "[[", "TotalPopSize")))
354 
+
355 
+
356 
+## cat("\n\n")
357 
+## cat("\n\n\n t_exp5 = ", t_exp5, "\n")
358 
+## object.size(exp5)/(Nindiv * 1024^2)
359 
+## cat("\n\n")
360 
+## summary(unlist(lapply(exp5, "[[", "NumClones")))
361 
+## summary(unlist(lapply(exp5, "[[", "NumIter")))
362 
+## summary(unlist(lapply(exp5, "[[", "FinalTime")))
363 
+## summary(unlist(lapply(exp5, "[[", "TotalPopSize")))
364 
+
365 
+
366 
+## cat("\n\n")
367 
+## cat("\n\n\n t_exp6 = ", t_exp6, "\n")
368 
+## object.size(exp6)/(Nindiv * 1024^2)
369 
+## cat("\n\n")
370 
+## summary(unlist(lapply(exp6, "[[", "NumClones")))
371 
+## summary(unlist(lapply(exp6, "[[", "NumIter")))
372 
+## summary(unlist(lapply(exp6, "[[", "FinalTime")))
373 
+## summary(unlist(lapply(exp6, "[[", "TotalPopSize")))
374 
+
375 
+
376 
+## ## Median runs until cancer
377 
+
378 
+## lapply(list(exp1, mc1, exp2, mc2, exp3, exp4, exp5, exp6),
379 
+##        function(y) median(unlist(lapply(y, function(x) x$other$attemptsUsed))))
380 
+
381 
+
382 
+
383 
+
384 
+
385 
+
386 
+## cat("\n\n\n t_exp1_noc = ", t_exp1_noc, "\n")
387 
+## object.size(exp1_noc)/(Nindiv * 1024^2)
388 
+## cat("\n\n")
389 
+## summary(unlist(lapply(exp1_noc, "[[", "NumClones")))
390 
+## summary(unlist(lapply(exp1_noc, "[[", "NumIter")))
391 
+## summary(unlist(lapply(exp1_noc, "[[", "FinalTime")))
392 
+## summary(unlist(lapply(exp1_noc, "[[", "TotalPopSize")))
393 
+
394 
+
395 
+## cat("\n\n")
396 
+## cat("\n\n\n t_mc1_noc = ", t_mc1_noc, "\n")
397 
+## object.size(mc1_noc)/(Nindiv * 1024^2)
398 
+## cat("\n\n")
399 
+## summary(unlist(lapply(mc1_noc, "[[", "NumClones")))
400 
+## summary(unlist(lapply(mc1_noc, "[[", "NumIter")))
401 
+## summary(unlist(lapply(mc1_noc, "[[", "FinalTime")))
402 
+## summary(unlist(lapply(mc1_noc, "[[", "TotalPopSize")))
403 
+
404 
+## cat("\n\n")
405 
+## cat("\n\n\n t_exp2_noc = ", t_exp2_noc, "\n")
406 
+## object.size(exp2_noc)/(Nindiv * 1024^2)
407 
+## cat("\n\n")
408 
+## summary(unlist(lapply(exp2_noc, "[[", "NumClones")))
409 
+## summary(unlist(lapply(exp2_noc, "[[", "NumIter")))
410 
+## summary(unlist(lapply(exp2_noc, "[[", "FinalTime")))
411 
+## summary(unlist(lapply(exp2_noc, "[[", "TotalPopSize")))
412 
+
413 
+## cat("\n\n")
414 
+## cat("\n\n\n t_mc2_noc = ", t_mc2_noc, "\n")
415 
+## object.size(mc2_noc)/(Nindiv * 1024^2)
416 
+## cat("\n\n")
417 
+## summary(unlist(lapply(mc2_noc, "[[", "NumClones")))
418 
+## summary(unlist(lapply(mc2_noc, "[[", "NumIter")))
419 
+## summary(unlist(lapply(mc2_noc, "[[", "FinalTime")))
420 
+## summary(unlist(lapply(mc2_noc, "[[", "TotalPopSize")))
421 
+
422 
+
423 
+
424 
+## cat("\n\n")
425 
+## cat("\n\n\n t_exp3_noc = ", t_exp3_noc, "\n")
426 
+## object.size(exp3_noc)/(Nindiv * 1024^2)
427 
+## cat("\n\n")
428 
+## summary(unlist(lapply(exp3_noc, "[[", "NumClones")))
429 
+## summary(unlist(lapply(exp3_noc, "[[", "NumIter")))
430 
+## summary(unlist(lapply(exp3_noc, "[[", "FinalTime")))
431 
+## summary(unlist(lapply(exp3_noc, "[[", "TotalPopSize")))
432 
+
433 
+
434 
+## cat("\n\n")
435 
+## cat("\n\n\n t_exp4_noc = ", t_exp4_noc, "\n")
436 
+## object.size(exp4_noc)/(Nindiv * 1024^2)
437 
+## cat("\n\n")
438 
+## summary(unlist(lapply(exp4_noc, "[[", "NumClones")))
439 
+## summary(unlist(lapply(exp4_noc, "[[", "NumIter")))
440 
+## summary(unlist(lapply(exp4_noc, "[[", "FinalTime")))
441 
+## summary(unlist(lapply(exp4_noc, "[[", "TotalPopSize")))
442 
+
443 
+
444 
+## cat("\n\n")
445 
+## cat("\n\n\n t_exp5_noc = ", t_exp5_noc, "\n")
446 
+## object.size(exp5_noc)/(Nindiv * 1024^2)
447 
+## cat("\n\n")
448 
+## summary(unlist(lapply(exp5_noc, "[[", "NumClones")))
449 
+## summary(unlist(lapply(exp5_noc, "[[", "NumIter")))
450 
+## summary(unlist(lapply(exp5_noc, "[[", "FinalTime")))
451 
+## summary(unlist(lapply(exp5_noc, "[[", "TotalPopSize")))
452 
+
453 
+
454 
+## cat("\n\n")
455 
+## cat("\n\n\n t_exp6_noc = ", t_exp6_noc, "\n")
456 
+## object.size(exp6_noc)/(Nindiv * 1024^2)
457 
+## cat("\n\n")
458 
+## summary(unlist(lapply(exp6_noc, "[[", "NumClones")))
459 
+## summary(unlist(lapply(exp6_noc, "[[", "NumIter")))
460 
+## summary(unlist(lapply(exp6_noc, "[[", "FinalTime")))
461 
+## summary(unlist(lapply(exp6_noc, "[[", "TotalPopSize")))
462 
+
463 
+
464 
+## ## Median runs until cancer
465 
+
466 
+## lapply(list(exp1_noc, mc1_noc, exp2_noc, mc2_noc, exp3_noc, exp4_noc, exp5_noc, exp6_noc),
467 
+##        function(y) median(unlist(lapply(y, function(x) x$other$attemptsUsed))))
468 
+
469 
+
470 
+
471 
+
472 
+
473 
+## ## bench1 <- data.frame(
474 
+## ##     time = c(t_exp1, t_mc1, t_exp2, t_mc2, t_exp3, t_exp4, t_exp5, t_exp6),
475 
+## ##     size = unlist(lapply(list(exp1, mc1, exp2, mc2, exp3, exp4, exp5, exp6),
476 
+## ##                          object.size))/(Nindiv * 1024^2),
477 
+
478 
+## ##     lapply(list(exp1, mc1, exp2, mc2, exp3, exp4, exp5, exp6),
479 
+## ##            function(y) median(unlist(lapply(y, function(x) x$other$attemptsUsed))))
480 
+## ## )
481 
+
482 
+
483 
+
484 
+
inst/miscell/benchmark_2.R
History View file @ fc38a87
0 485 
new file mode 100644
... ... 
@@ -0,0 +1,223 @@
1 
+### Benchmark runs. This produces "benchmark_1.RData"
2 
+
3 
+
4 
+rm(list = ls())
5 
+set.seed(NULL)
6 
+
7 
+library(OncoSimulR)
8 
+
9 
+######################################################################
10 
+######################################################################
11 
+######################################################################
12 
+######################################################################
13 
+system_summary <- function() {
14 
+    return(list(versioninfo = version,
15 
+                memimfo = system("free", intern = TRUE),
16 
+                cpuinfo = system("cat /proc/cpuinfo | grep 'model name'", intern = TRUE),
17 
+                packageinfo = paste("OncoSimulR, ", packageVersion("OncoSimulR")),
18 
+##                nodeinfo = Sys.info()$nodename,
19 
+##                nodelinuxinfo = paste(Sys.info()$sysname, Sys.info()$release),
20 
+                dateinfo = date()))
21 
+}
22 
+
23 
+stats_simuls <- function(sim) {
24 
+    ## sim is an oncoSimulPop output
25 
+    trf <- function(x) {
26