git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/OncoSimulR@109070 bc3139a8-67e5-0310-9ffc-ced21a209358
Ramon Diaz-Uriarte authored on 01/10/2015 12:49:30
| ... | ... |
@@ -2,7 +2,7 @@ Package: OncoSimulR |
| 2 | 2 |
Type: Package |
| 3 | 3 |
Title: Forward Genetic Simulation of Cancer Progresion with Epistasis |
| 4 | 4 |
Version: 1.99.8 |
| 5 |
-Date: 2015-10-01 |
|
| 5 |
+Date: 2015-30-01 |
|
| 6 | 6 |
Author: Ramon Diaz-Uriarte. |
| 7 | 7 |
Maintainer: Ramon Diaz-Uriarte <rdiaz02@gmail.com> |
| 8 | 8 |
Description: Functions for forward population genetic simulation in |
| ... | ... |
@@ -648,8 +648,8 @@ void addToPhylog(PhylogName& phylog, |
| 648 | 648 |
|
| 649 | 649 |
static void nr_innerBNB(const fitnessEffectsAll& fitnessEffects, |
| 650 | 650 |
const double& initSize, |
| 651 |
- const double& K, |
|
| 652 |
- const double& alpha, |
|
| 651 |
+ const double& K, |
|
| 652 |
+ const double& alpha, |
|
| 653 | 653 |
const double& genTime, |
| 654 | 654 |
const TypeModel typeModel, |
| 655 | 655 |
const int& mutatorGenotype, |
| ... | ... |
@@ -665,28 +665,28 @@ static void nr_innerBNB(const fitnessEffectsAll& fitnessEffects, |
| 665 | 665 |
const int& detectionDrivers, |
| 666 | 666 |
const double& minDetectDrvCloneSz, |
| 667 | 667 |
const double& extraTime, |
| 668 |
- const int& verbosity, |
|
| 669 |
- double& totPopSize, |
|
| 670 |
- double& e1, |
|
| 671 |
- double& n_0, |
|
| 672 |
- double& n_1, |
|
| 673 |
- double& ratioForce, |
|
| 674 |
- double& currentTime, |
|
| 675 |
- int& speciesFS, |
|
| 676 |
- int& outNS_i, |
|
| 677 |
- int& iter, |
|
| 678 |
- std::vector<Genotype>& genot_out, |
|
| 679 |
- std::vector<double>& popSizes_out, |
|
| 680 |
- std::vector<int>& index_out, |
|
| 681 |
- std::vector<double>& time_out, |
|
| 682 |
- std::vector<double>& sampleTotPopSize, |
|
| 683 |
- std::vector<double>& sampleLargestPopSize, |
|
| 684 |
- std::vector<int>& sampleMaxNDr, |
|
| 685 |
- std::vector<int>& sampleNDrLargestPop, |
|
| 686 |
- bool& reachDetection, |
|
| 668 |
+ const int& verbosity, |
|
| 669 |
+ double& totPopSize, |
|
| 670 |
+ double& e1, |
|
| 671 |
+ double& n_0, |
|
| 672 |
+ double& n_1, |
|
| 673 |
+ double& ratioForce, |
|
| 674 |
+ double& currentTime, |
|
| 675 |
+ int& speciesFS, |
|
| 676 |
+ int& outNS_i, |
|
| 677 |
+ int& iter, |
|
| 678 |
+ std::vector<Genotype>& genot_out, |
|
| 679 |
+ std::vector<double>& popSizes_out, |
|
| 680 |
+ std::vector<int>& index_out, |
|
| 681 |
+ std::vector<double>& time_out, |
|
| 682 |
+ std::vector<double>& sampleTotPopSize, |
|
| 683 |
+ std::vector<double>& sampleLargestPopSize, |
|
| 684 |
+ std::vector<int>& sampleMaxNDr, |
|
| 685 |
+ std::vector<int>& sampleNDrLargestPop, |
|
| 686 |
+ bool& reachDetection, |
|
| 687 | 687 |
std::mt19937& ran_gen, |
| 688 | 688 |
// randutils::mt19937_rng& ran_gen, |
| 689 |
- double& runningWallTime, |
|
| 689 |
+ double& runningWallTime, |
|
| 690 | 690 |
bool& hittedWallTime, |
| 691 | 691 |
const std::map<int, std::string>& intName, |
| 692 | 692 |
const fitness_as_genes& genesInFitness, |
| ... | ... |
@@ -1287,6 +1287,8 @@ static void nr_innerBNB(const fitnessEffectsAll& fitnessEffects, |
| 1287 | 1287 |
adjust_fitness_B, adjust_fitness_MF); |
| 1288 | 1288 |
|
| 1289 | 1289 |
if(tmpParam.birth > 0.0) {
|
| 1290 |
+ // if(keepMutationTimes) |
|
| 1291 |
+ // update_mutation_freqs(newMutation, currentTime, mutation_freq_at); |
|
| 1290 | 1292 |
//FIXME: phylog |
| 1291 | 1293 |
if(keepPhylog) |
| 1292 | 1294 |
addToPhylog(phylog, Genotypes[nextMutant], newGenotype, currentTime, |
| ... | ... |
@@ -164,7 +164,7 @@ test_that("initMutant non lexicog order",
|
| 164 | 164 |
expect_true( "m, d, f_u" %in% cn ) |
| 165 | 165 |
}) |
| 166 | 166 |
|
| 167 |
- |
|
| 167 |
+## FIXME: we could use stronger test: we will never see M > D |
|
| 168 | 168 |
test_that("initMutant with oncoSimulSample", {
|
| 169 | 169 |
o3init <- allFitnessEffects(orderEffects = c( |
| 170 | 170 |
"M > D > F" = 0.99, |
| ... | ... |
@@ -187,7 +187,7 @@ test_that("initMutant with oncoSimulSample", {
|
| 187 | 187 |
mu = 5e-5, finalTime = 5000, |
| 188 | 188 |
detectionDrivers = 2, |
| 189 | 189 |
onlyCancer = TRUE, |
| 190 |
- initSize = 10, |
|
| 190 |
+ initSize = 500, |
|
| 191 | 191 |
initMutant = c("z > d"),
|
| 192 | 192 |
thresholdWhole = 1 ## check presence of initMutant |
| 193 | 193 |
) |
| ... | ... |
@@ -202,52 +202,56 @@ test_that("initMutant with oncoSimulSample", {
|
| 202 | 202 |
as.character(ossI$popSummary$OccurringDrivers)), 1:4) |
| 203 | 203 |
}) |
| 204 | 204 |
|
| 205 |
-## This fails less than 1 in /10000. Handle reliably |
|
| 206 |
-## test_that("initMutant with oncoSimulSample, 2", {
|
|
| 207 |
-## o3init <- allFitnessEffects(orderEffects = c( |
|
| 208 |
-## "M > D > F" = 0.99, |
|
| 209 |
-## "D > M > F" = 0.2, |
|
| 210 |
-## "D > M" = 0.1, |
|
| 211 |
-## "M > D" = 0.9, |
|
| 212 |
-## "M > A" = 0.25), |
|
| 213 |
-## noIntGenes = c("u" = 0.01,
|
|
| 214 |
-## "v" = 0.01, |
|
| 215 |
-## "w" = 0.001, |
|
| 216 |
-## "x" = 0.0001, |
|
| 217 |
-## "y" = -0.0001, |
|
| 218 |
-## "z" = -0.001), |
|
| 219 |
-## geneToModule = |
|
| 220 |
-## c("Root" = "Root",
|
|
| 221 |
-## "A" = "a", |
|
| 222 |
-## "M" = "m", |
|
| 223 |
-## "F" = "f", |
|
| 224 |
-## "D" = "d") ) |
|
| 225 |
-## ossI <- oncoSimulSample(4, |
|
| 226 |
-## o3init, model = "Exp", |
|
| 227 |
-## mu = 5e-5, finalTime = 5000, |
|
| 228 |
-## detectionDrivers = 3, |
|
| 229 |
-## onlyCancer = TRUE, |
|
| 230 |
-## initSize = 10, |
|
| 231 |
-## initMutant = c("z > a"),
|
|
| 232 |
-## thresholdWhole = 1 ## check presence of initMutant |
|
| 233 |
-## ) |
|
| 234 |
-## ssp <- ossI$popSample |
|
| 235 |
-## expect_equal(ssp[, c("a", "z")],
|
|
| 236 |
-## matrix(1, nrow = 4, ncol = 2, |
|
| 237 |
-## dimnames = list(NULL, c("a", "z"))))
|
|
| 238 |
-## expect_false(sum(ssp) == prod(dim(ssp))) ## we don't just have all of |
|
| 239 |
-## ## them, which would turn the |
|
| 240 |
-## ## previous into irrelevant |
|
| 241 |
-## expect_equal(grep("a",
|
|
| 242 |
-## as.character(ossI$popSummary$OccurringDrivers)), 1:4) |
|
| 243 |
-## }) |
|
| 205 |
+ |
|
| 206 |
+test_that("initMutant with oncoSimulSample, 2", {
|
|
| 207 |
+ o3init <- allFitnessEffects(orderEffects = c( |
|
| 208 |
+ "M > D > F" = 0.99, |
|
| 209 |
+ "D > M > F" = 0.2, |
|
| 210 |
+ "D > M" = 0.1, |
|
| 211 |
+ "M > D" = 0.9, |
|
| 212 |
+ "M > A" = 0.25, |
|
| 213 |
+ "A > H" = 0.2, |
|
| 214 |
+ "A > G" = 0.3), |
|
| 215 |
+ noIntGenes = c("u" = 0.1,
|
|
| 216 |
+ "v" = 0.2, |
|
| 217 |
+ "w" = 0.001, |
|
| 218 |
+ "x" = 0.0001, |
|
| 219 |
+ "y" = -0.0001, |
|
| 220 |
+ "z" = -0.001), |
|
| 221 |
+ geneToModule = |
|
| 222 |
+ c("Root" = "Root",
|
|
| 223 |
+ "A" = "a", |
|
| 224 |
+ "M" = "m", |
|
| 225 |
+ "F" = "f", |
|
| 226 |
+ "D" = "d", |
|
| 227 |
+ "H" = "h", |
|
| 228 |
+ "G" = "g") ) |
|
| 229 |
+ ossI <- oncoSimulSample(4, |
|
| 230 |
+ o3init, model = "Exp", |
|
| 231 |
+ mu = 5e-5, finalTime = 5000, |
|
| 232 |
+ detectionDrivers = 3, |
|
| 233 |
+ onlyCancer = TRUE, |
|
| 234 |
+ initSize = 500, |
|
| 235 |
+ initMutant = c("z > a"),
|
|
| 236 |
+ thresholdWhole = 1 ## check presence of initMutant |
|
| 237 |
+ ) |
|
| 238 |
+ ssp <- ossI$popSample |
|
| 239 |
+ expect_equal(ssp[, c("a", "z")],
|
|
| 240 |
+ matrix(1, nrow = 4, ncol = 2, |
|
| 241 |
+ dimnames = list(NULL, c("a", "z"))))
|
|
| 242 |
+ expect_false(sum(ssp) == prod(dim(ssp))) ## we don't just have all of |
|
| 243 |
+ ## them, which would turn the |
|
| 244 |
+ ## previous into irrelevant |
|
| 245 |
+ expect_equal(grep("a",
|
|
| 246 |
+ as.character(ossI$popSummary$OccurringDrivers)), 1:4) |
|
| 247 |
+}) |
|
| 244 | 248 |
|
| 245 | 249 |
|
| 246 | 250 |
test_that("initMutant with oncoSimulPop", {
|
| 247 | 251 |
o3init <- allFitnessEffects(orderEffects = c( |
| 248 | 252 |
"M > D > F" = 0.99, |
| 249 | 253 |
"D > M > F" = 0.2, |
| 250 |
- "D > M" = 0.1, |
|
| 254 |
+ "D > M" = 0.2, |
|
| 251 | 255 |
"M > D" = 0.9), |
| 252 | 256 |
noIntGenes = c("u" = 0.01,
|
| 253 | 257 |
"v" = 0.01, |
| ... | ... |
@@ -262,11 +266,11 @@ test_that("initMutant with oncoSimulPop", {
|
| 262 | 266 |
"D" = "d") ) |
| 263 | 267 |
ospI <- oncoSimulPop(4, |
| 264 | 268 |
o3init, model = "Exp", |
| 265 |
- mu = 5e-5, finalTime = 1000, |
|
| 269 |
+ mu = 5e-5, finalTime = 5000, |
|
| 266 | 270 |
detectionDrivers = 3, |
| 267 | 271 |
onlyCancer = TRUE, |
| 268 | 272 |
keepPhylog = TRUE, |
| 269 |
- initSize = 10, |
|
| 273 |
+ initSize = 500, |
|
| 270 | 274 |
initMutant = c("d > m > y"),
|
| 271 | 275 |
mc.cores = 2 |
| 272 | 276 |
) |
| ... | ... |
@@ -302,7 +306,7 @@ test_that("initMutant with oncoSimulPop, 2", {
|
| 302 | 306 |
"D > M > F" = 0.2, |
| 303 | 307 |
"D > M" = 0.1, |
| 304 | 308 |
"M > D" = 0.9), |
| 305 |
- noIntGenes = c("u" = 0.01,
|
|
| 309 |
+ noIntGenes = c("u" = 0.01,
|
|
| 306 | 310 |
"v" = 0.01, |
| 307 | 311 |
"w" = 0.001, |
| 308 | 312 |
"x" = 0.0001, |
| ... | ... |
@@ -319,7 +323,7 @@ test_that("initMutant with oncoSimulPop, 2", {
|
| 319 | 323 |
detectionDrivers = 4, ## yes, reach end |
| 320 | 324 |
onlyCancer = FALSE, |
| 321 | 325 |
keepPhylog = TRUE, |
| 322 |
- initSize = 10, |
|
| 326 |
+ initSize = 100, |
|
| 323 | 327 |
initMutant = c("m > v > d"),
|
| 324 | 328 |
mc.cores = 2 |
| 325 | 329 |
) |
| ... | ... |
@@ -164,7 +164,7 @@ test_that("exercising oncoSimulSample, new format", {
|
| 164 | 164 |
test_that("check error unknown timeSample", {
|
| 165 | 165 |
data(examplePosets) |
| 166 | 166 |
p701 <- examplePosets[["p701"]] |
| 167 |
- r1 <- oncoSimulIndiv(p701, onlyCancer = TRUE) |
|
| 167 |
+ r1 <- oncoSimulIndiv(p701, onlyCancer = TRUE, max.num.tries = 5000) |
|
| 168 | 168 |
expect_error(samplePop(r1, timeSample = "uniformo"), |
| 169 | 169 |
"Unknown timeSample option") |
| 170 | 170 |
expect_error(samplePop(r1, timeSample = "uni"), |
| ... | ... |
@@ -182,7 +182,7 @@ test_that("check error unknown timeSample", {
|
| 182 | 182 |
test_that("check error unknown typeSample", {
|
| 183 | 183 |
data(examplePosets) |
| 184 | 184 |
p701 <- examplePosets[["p701"]] |
| 185 |
- r1 <- oncoSimulIndiv(p701, onlyCancer = TRUE) |
|
| 185 |
+ r1 <- oncoSimulIndiv(p701, onlyCancer = TRUE, max.num.tries = 5000) |
|
| 186 | 186 |
expect_error(samplePop(r1, typeSample = "uniformo"), |
| 187 | 187 |
"Unknown typeSample option") |
| 188 | 188 |
expect_error(samplePop(r1, typeSample = "uni"), |
| ... | ... |
@@ -6,7 +6,7 @@ nindiv <- 4 |
| 6 | 6 |
|
| 7 | 7 |
|
| 8 | 8 |
test_that("oncoSimulSample success with large num tries", {
|
| 9 |
- p1 <- oncoSimulSample(nindiv, p701, max.num.tries = 200 * nindiv, |
|
| 9 |
+ p1 <- oncoSimulSample(nindiv, p701, max.num.tries = 5000 * nindiv, |
|
| 10 | 10 |
onlyCancer = TRUE) |
| 11 | 11 |
expect_true(p1$probCancer < 1) |
| 12 | 12 |
expect_true(p1$attemptsUsed > nindiv) |
| ... | ... |
@@ -37,7 +37,7 @@ test_that("oncoSimulSample exits with minimal num tries", {
|
| 37 | 37 |
|
| 38 | 38 |
test_that("oncoSimulSample exits with small num tries", {
|
| 39 | 39 |
p6 <- oncoSimulSample(nindiv, p701, |
| 40 |
- max.num.tries = nindiv + 4, |
|
| 40 |
+ max.num.tries = nindiv + 2, |
|
| 41 | 41 |
onlyCancer = TRUE) |
| 42 | 42 |
expect_true(p6$HittedMaxTries) |
| 43 | 43 |
expect_true(is.na(p6$popSummary)) |
| ... | ... |
@@ -197,13 +197,13 @@ test_that("exercising the sampling code, v2 objects", {
|
| 197 | 197 |
"F" = "f1, f2, f3", |
| 198 | 198 |
"D" = "d1, d2") ) |
| 199 | 199 |
o1 <- oncoSimulIndiv(oi, detectionSize = 1e4, |
| 200 |
- onlyCancer = TRUE) |
|
| 200 |
+ onlyCancer = TRUE, |
|
| 201 |
+ max.num.tries = 5000) |
|
| 201 | 202 |
o4 <- oncoSimulPop(2, |
| 202 | 203 |
oi, |
| 203 | 204 |
detectionSize = 1e4, |
| 204 |
- onlyCancer = TRUE) |
|
| 205 |
- ## many of them are generating warnings, because sampling |
|
| 206 |
- ## with pop size of 0. That is OK. |
|
| 205 |
+ onlyCancer = TRUE, |
|
| 206 |
+ max.num.tries = 5000) |
|
| 207 | 207 |
expect_message(samplePop(o1), |
| 208 | 208 |
"Subjects by Genes matrix of 1 subjects and 10 genes") |
| 209 | 209 |
expect_message(samplePop(o1, typeSample = "single", |
| ... | ... |
@@ -242,14 +242,14 @@ test_that("exercising the sampling code, v2 objects, more", {
|
| 242 | 242 |
typeDep = "MN") |
| 243 | 243 |
cbn1 <- allFitnessEffects(cs) |
| 244 | 244 |
o1 <- oncoSimulIndiv(cbn1, detectionSize = 1e4, |
| 245 |
- onlyCancer = TRUE) |
|
| 245 |
+ onlyCancer = TRUE, |
|
| 246 |
+ max.num.tries = 5000) |
|
| 246 | 247 |
o4 <- oncoSimulPop(4, |
| 247 | 248 |
cbn1, |
| 248 | 249 |
detectionSize = 1e4, |
| 249 | 250 |
onlyCancer = TRUE, |
| 250 |
- mc.cores = 2) |
|
| 251 |
- ## many of them are generating warnings, because sampling |
|
| 252 |
- ## with pop size of 0. That is OK. |
|
| 251 |
+ mc.cores = 2, |
|
| 252 |
+ max.num.tries = 5000) |
|
| 253 | 253 |
expect_message(samplePop(o1), |
| 254 | 254 |
"Subjects by Genes matrix of 1 subjects and 6 genes") |
| 255 | 255 |
expect_message(samplePop(o1, typeSample = "single", |
| ... | ... |
@@ -1,15 +1,15 @@ |
| 1 | 1 |
\usepackage[% |
| 2 |
- shash={ea5bee4},
|
|
| 3 |
- lhash={ea5bee4855766a04a04786475d269790a28c8373},
|
|
| 2 |
+ shash={d3b5a5a},
|
|
| 3 |
+ lhash={d3b5a5acc1dd65694d086183fbf8b7fd2919c025},
|
|
| 4 | 4 |
authname={ramon diaz-uriarte (at Bufo)},
|
| 5 | 5 |
authemail={rdiaz02@gmail.com},
|
| 6 |
- authsdate={2015-09-27},
|
|
| 7 |
- authidate={2015-09-27 14:25:22 +0200},
|
|
| 8 |
- authudate={1443356722},
|
|
| 6 |
+ authsdate={2015-10-01},
|
|
| 7 |
+ authidate={2015-10-01 14:42:40 +0200},
|
|
| 8 |
+ authudate={1443703360},
|
|
| 9 | 9 |
commname={ramon diaz-uriarte (at Bufo)},
|
| 10 | 10 |
commemail={rdiaz02@gmail.com},
|
| 11 |
- commsdate={2015-09-27},
|
|
| 12 |
- commidate={2015-09-27 14:25:22 +0200},
|
|
| 13 |
- commudate={1443356722},
|
|
| 11 |
+ commsdate={2015-10-01},
|
|
| 12 |
+ commidate={2015-10-01 14:42:40 +0200},
|
|
| 13 |
+ commudate={1443703360},
|
|
| 14 | 14 |
refnames={ (HEAD -> master)}
|
| 15 | 15 |
]{gitsetinfo}
|
| 16 | 16 |
\ No newline at end of file |