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Deprecate arguments no.bootstraps and bootstrap.percent, simplify return value to the object storing the enrichment scores and add argument return.old.value to provide backward compability for the return value during the next release.

[rcastelo] authored on 06/09/2017 15:00:02
Showing 6 changed files

... ...
@@ -1,5 +1,5 @@
1 1
 Package: GSVA
2
-Version: 1.25.5
2
+Version: 1.25.6
3 3
 Title: Gene Set Variation Analysis for microarray and RNA-seq data
4 4
 Authors@R: c(person("Justin", "Guinney", role=c("aut", "cre"), email="justin.guinney@sagebase.org"),
5 5
              person("Robert", "Castelo", role="aut", email="robert.castelo@upf.edu"),
... ...
@@ -9,19 +9,20 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="list"),
9 9
           function(expr, gset.idx.list, annotation,
10 10
   method=c("gsva", "ssgsea", "zscore", "plage"),
11 11
   kcdf=c("Gaussian", "Poisson", "none"),
12
-  rnaseq=FALSE,
12
+  rnaseq=FALSE, ## deprecated
13 13
   abs.ranking=FALSE,
14 14
   min.sz=1,
15 15
   max.sz=Inf,
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-  no.bootstraps=0, 
17
-  bootstrap.percent = .632, 
16
+  no.bootstraps=0, ## deprecated
17
+  bootstrap.percent = .632, ## deprecated
18 18
   parallel.sz=0, 
19 19
   parallel.type="SOCK",
20 20
   mx.diff=TRUE,
21 21
   tau=switch(method, gsva=1, ssgsea=0.25, NA),
22
-  kernel=TRUE,
22
+  kernel=TRUE, ## deprecated
23 23
   ssgsea.norm=TRUE,
24
-  verbose=TRUE)
24
+  verbose=TRUE,
25
+  return.old.value=FALSE) ## transient argument for deprecating 'no.bootstraps' and 'bootstrap.percent'
25 26
 {
26 27
   method <- match.arg(method)
27 28
   kcdf <- match.arg(kcdf)
... ...
@@ -32,6 +33,9 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="list"),
32 33
   if (!missing(kernel))
33 34
     warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.")
34 35
 
36
+  if (no.bootstraps > 0)
37
+    warning("The argument 'no.bootstraps' is deprecated and will be removed in the next release of GSVA. This implies that the 'gsva()' function with the default argument 'method=\"gsva\"' only returns a matrix of GSVA enrichment scores. To obtain the same output in the form of a list as in previous versions you can set 'return.old.value=TRUE' during this release but this argument will not be available anymore in the next release.")
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+
35 39
   ## filter out genes with constant expression values
36 40
   sdGenes <- Biobase::esApply(expr, 1, sd)
37 41
   if (any(sdGenes == 0) || any(is.na(sdGenes))) {
... ...
@@ -81,28 +85,31 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="list"),
81 85
   eScoEset <- new("ExpressionSet", exprs=eSco$es.obs, phenoData=phenoData(expr),
82 86
                   experimentData=experimentData(expr), annotation="")
83 87
 
84
-	return(list(es.obs=eScoEset,
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-				      bootstrap=eSco$bootstrap,
86
-              p.vals.sign=eSco$p.vals.sign))
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+  rval <- eScoEset
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+  if (return.old.value) ## to be removed in the next release
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+    rval <- list(es.obs=eScoEset, bootstrap=eSco$bootstrap, p.vals.sign=eSco$p.vals.sign)
91
+
92
+  rval
87 93
 })
88 94
 
89 95
 setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="GeneSetCollection"),
90 96
           function(expr, gset.idx.list, annotation,
91 97
   method=c("gsva", "ssgsea", "zscore", "plage"),
92 98
   kcdf=c("Gaussian", "Poisson", "none"),
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-  rnaseq=FALSE,
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+  rnaseq=FALSE, ## deprecated
94 100
   abs.ranking=FALSE,
95 101
   min.sz=1,
96 102
   max.sz=Inf,
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-  no.bootstraps=0, 
98
-  bootstrap.percent = .632, 
103
+  no.bootstraps=0, ## deprecated
104
+  bootstrap.percent = .632, ## deprecated
99 105
   parallel.sz=0, 
100 106
   parallel.type="SOCK",
101 107
   mx.diff=TRUE,
102 108
   tau=switch(method, gsva=1, ssgsea=0.25, NA),
103
-  kernel=TRUE,
109
+  kernel=TRUE, ## deprecated
104 110
   ssgsea.norm=TRUE,
105
-  verbose=TRUE)
111
+  verbose=TRUE,
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+  return.old.value=FALSE) ## transient argument for deprecating 'no.bootstraps' and 'bootstrap.percent'
106 113
 {
107 114
   method <- match.arg(method)
108 115
   kcdf <- match.arg(kcdf)
... ...
@@ -113,6 +120,9 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="GeneSetCollecti
113 120
   if (!missing(kernel))
114 121
     warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.")
115 122
 
123
+  if (no.bootstraps > 0)
124
+    warning("The argument 'no.bootstraps' is deprecated and will be removed in the next release of GSVA. This implies that the 'gsva()' function with the default argument 'method=\"gsva\"' only returns a matrix of GSVA enrichment scores. To obtain the same output in the form of a list as in previous versions you can set 'return.old.value=TRUE' during this release but this argument will not be available anymore in the next release.")
125
+
116 126
   ## filter out genes with constant expression values
117 127
   sdGenes <- Biobase::esApply(expr, 1, sd)
118 128
   if (any(sdGenes == 0) || any(is.na(sdGenes))) {
... ...
@@ -166,28 +176,31 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="GeneSetCollecti
166 176
   eScoEset <- new("ExpressionSet", exprs=eSco$es.obs, phenoData=phenoData(expr),
167 177
                   experimentData=experimentData(expr), annotation="")
168 178
 
169
-	return(list(es.obs=eScoEset,
170
-				      bootstrap=eSco$bootstrap,
171
-              p.vals.sign=eSco$p.vals.sign))
179
+  rval <- eScoEset
180
+  if (return.old.value) ## to be removed in the next release
181
+    rval <- list(es.obs=eScoEset, bootstrap=eSco$bootstrap, p.vals.sign=eSco$p.vals.sign)
182
+
183
+  rval
172 184
 })
173 185
 
174 186
 setMethod("gsva", signature(expr="matrix", gset.idx.list="GeneSetCollection"),
175 187
           function(expr, gset.idx.list, annotation,
176 188
   method=c("gsva", "ssgsea", "zscore", "plage"),
177 189
   kcdf=c("Gaussian", "Poisson", "none"),
178
-  rnaseq=FALSE,
190
+  rnaseq=FALSE, ## deprecated
179 191
   abs.ranking=FALSE,
180 192
   min.sz=1,
181 193
   max.sz=Inf,
182
-  no.bootstraps=0, 
183
-  bootstrap.percent = .632, 
194
+  no.bootstraps=0, ## deprecated
195
+  bootstrap.percent = .632,  ## deprecated
184 196
   parallel.sz=0, 
185 197
   parallel.type="SOCK",
186 198
   mx.diff=TRUE,
187 199
   tau=switch(method, gsva=1, ssgsea=0.25, NA),
188
-  kernel=TRUE,
200
+  kernel=TRUE, ## deprecated
189 201
   ssgsea.norm=TRUE,
190
-  verbose=TRUE)
202
+  verbose=TRUE,
203
+  return.old.value=FALSE) ## transient argument for deprecating 'no.bootstraps' and 'bootstrap.percent'
191 204
 {
192 205
   method <- match.arg(method)
193 206
   kcdf <- match.arg(kcdf)
... ...
@@ -198,6 +211,9 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="GeneSetCollection"),
198 211
   if (!missing(kernel))
199 212
     warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.")
200 213
 
214
+  if (no.bootstraps > 0)
215
+    warning("The argument 'no.bootstraps' is deprecated and will be removed in the next release of GSVA. This implies that the 'gsva()' function with the default argument 'method=\"gsva\"' only returns a matrix of GSVA enrichment scores. To obtain the same output in the form of a list as in previous versions you can set 'return.old.value=TRUE' during this release but this argument will not be available anymore in the next release.")
216
+
201 217
   ## filter out genes with constant expression values
202 218
   sdGenes <- apply(expr, 1, sd)
203 219
   if (any(sdGenes == 0) || any(is.na(sdGenes))) {
... ...
@@ -248,28 +264,34 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="GeneSetCollection"),
248 264
       kernel <- FALSE
249 265
   }
250 266
 
251
-  .gsva(expr, mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking,
252
-        no.bootstraps, bootstrap.percent, parallel.sz, parallel.type,
253
-        mx.diff, tau, kernel, ssgsea.norm, verbose)
267
+  rval <- .gsva(expr, mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking,
268
+                no.bootstraps, bootstrap.percent, parallel.sz, parallel.type,
269
+                mx.diff, tau, kernel, ssgsea.norm, verbose)
270
+
271
+  if (method == "gsva" && !return.old.value) ## to be removed in the next release
272
+    rval <- rval$es.obs
273
+
274
+  rval
254 275
 })
255 276
 
256 277
 setMethod("gsva", signature(expr="matrix", gset.idx.list="list"),
257 278
           function(expr, gset.idx.list, annotation,
258 279
   method=c("gsva", "ssgsea", "zscore", "plage"),
259 280
   kcdf=c("Gaussian", "Poisson", "none"),
260
-  rnaseq=FALSE,
281
+  rnaseq=FALSE, ## deprecated
261 282
   abs.ranking=FALSE,
262 283
   min.sz=1,
263 284
   max.sz=Inf,
264
-  no.bootstraps=0, 
265
-  bootstrap.percent = .632, 
285
+  no.bootstraps=0, ## deprecated
286
+  bootstrap.percent = .632, ## deprecated
266 287
   parallel.sz=0, 
267 288
   parallel.type="SOCK",
268 289
   mx.diff=TRUE,
269 290
   tau=switch(method, gsva=1, ssgsea=0.25, NA),
270
-  kernel=TRUE,
291
+  kernel=TRUE, ## deprecated
271 292
   ssgsea.norm=TRUE,
272
-  verbose=TRUE)
293
+  verbose=TRUE,
294
+  return.old.value=FALSE) ## transient argument for deprecating 'no.bootstraps' and 'bootstrap.percent'
273 295
 {
274 296
   method <- match.arg(method)
275 297
   kcdf <- match.arg(kcdf)
... ...
@@ -280,6 +302,9 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="list"),
280 302
   if (!missing(kernel))
281 303
     warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.")
282 304
 
305
+  if (no.bootstraps > 0)
306
+    warning("The argument 'no.bootstraps' is deprecated and will be removed in the next release of GSVA. This implies that the 'gsva()' function with the default argument 'method=\"gsva\"' only returns a matrix of GSVA enrichment scores. To obtain the same output in the form of a list as in previous versions you can set 'return.old.value=TRUE' during this release but this argument will not be available anymore in the next release.")
307
+
283 308
   ## filter out genes with constant expression values
284 309
   sdGenes <- apply(expr, 1, sd)
285 310
   if (any(sdGenes == 0) || any(is.na(sdGenes))) {
... ...
@@ -318,9 +343,14 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="list"),
318 343
       kernel <- FALSE
319 344
   }
320 345
 
321
-  .gsva(expr, mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking, no.bootstraps,
322
-        bootstrap.percent, parallel.sz, parallel.type,
323
-        mx.diff, tau, kernel, ssgsea.norm, verbose)
346
+  rval <- .gsva(expr, mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking, no.bootstraps,
347
+                bootstrap.percent, parallel.sz, parallel.type,
348
+                mx.diff, tau, kernel, ssgsea.norm, verbose)
349
+
350
+  if (method == "gsva" && !return.old.value) ## to be removed in the next release
351
+    rval <- rval$es.obs
352
+
353
+  rval
324 354
 })
325 355
 
326 356
 .gsva <- function(expr, gset.idx.list,
... ...
@@ -452,7 +482,7 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="list"),
452 482
 			n.cycles <- floor(no.bootstraps / parallel.sz)
453 483
 			for(i in 1:n.cycles){
454 484
 				if(verbose) cat("bootstrap cycle ", i, "\n")
455
-				r <- clEvalQ(cl, GSVA:::compute.geneset.es(expr, gset.idx.list, 
485
+				r <- clEvalQ(cl, compute.geneset.es(expr, gset.idx.list, 
456 486
 								sample(n.samples, bootstrap.nsamples, replace=T),
457 487
 								rnaseq=rnaseq, abs.ranking=abs.ranking, mx.diff=mx.diff,
458 488
                 tau=tau, kernel=kernel, verbose=FALSE, parallel.sz=1))
... ...
@@ -271,17 +271,17 @@ gsva_information <- function(input, output, session) {
271 271
   else
272 272
   {
273 273
 
274
-    resultInformation <- matrix(data = c(input$matrixVar,input$genesetVar,ncol(generated_gsva$es.obs),nrow(generated_gsva$es.obs)), nrow = 1, ncol = 4)
274
+    resultInformation <- matrix(data = c(input$matrixVar,input$genesetVar,ncol(generated_gsva),nrow(generated_gsva)), nrow = 1, ncol = 4)
275 275
     colnames(resultInformation) <- c("Matrix used","GeneSet used", "Col num", "Row num")
276 276
     output$result <- renderTable(resultInformation)
277
-    if(class(generated_gsva$es.obs) == "ExpressionSet") #If the generated gsva is an ExpressionSet
277
+    if(class(generated_gsva) == "ExpressionSet") #If the generated gsva is an ExpressionSet
278 278
     {
279
-      expressionSetObs <- exprs(generated_gsva$es.obs)
279
+      expressionSetObs <- exprs(generated_gsva)
280 280
       output$plot <- renderPlot(multidensity(as.list(as.data.frame(expressionSetObs)), legend=NA, las=1, xlab=sprintf("%s scores", input$method), main="", lwd=2)) 
281 281
     }
282 282
     else
283 283
     {
284
-      output$plot <- renderPlot(multidensity(as.list(as.data.frame(generated_gsva$es.obs)), legend=NA, las=1, xlab=sprintf("%s scores", input$method), main="", lwd=2))
284
+      output$plot <- renderPlot(multidensity(as.list(as.data.frame(generated_gsva)), legend=NA, las=1, xlab=sprintf("%s scores", input$method), main="", lwd=2))
285 285
     }
286 286
     tagList(
287 287
       downloadButton('downloadData', 'Download'),
... ...
@@ -304,15 +304,15 @@ download_handler <- function(input, output, session) {
304 304
       }
305 305
       else
306 306
       {
307
-        if(class(generated_gsva$es.obs) == "ExpressionSet") #If the generated gsva es.obs is an ExpressionSet
307
+        if(class(generated_gsva) == "ExpressionSet") #If the generated gsva result value is an ExpressionSet
308 308
         {
309
-          expressionSetObs <- exprs(generated_gsva$es.obs)
309
+          expressionSetObs <- exprs(generated_gsva)
310 310
           dataFrameObs <- as.data.frame(expressionSetObs)
311 311
           write.csv(dataFrameObs, file)
312 312
         }
313 313
         else
314 314
         {
315
-          dataFrameObs <- as.data.frame(generated_gsva$es.obs)
315
+          dataFrameObs <- as.data.frame(generated_gsva)
316 316
           write.csv(dataFrameObs, file)
317 317
         } 
318 318
       }
319 319
Binary files a/inst/extdata/cache4vignette_leukemia_es.RData and b/inst/extdata/cache4vignette_leukemia_es.RData differ
... ...
@@ -29,7 +29,8 @@ Estimates GSVA enrichment scores.
29 29
     tau=switch(method, gsva=1, ssgsea=0.25, NA),
30 30
     kernel=TRUE,
31 31
     ssgsea.norm=TRUE,
32
-    verbose=TRUE)
32
+    verbose=TRUE,
33
+    return.old.value=FALSE)
33 34
 \S4method{gsva}{ExpressionSet,GeneSetCollection}(expr, gset.idx.list, annotation,
34 35
     method=c("gsva", "ssgsea", "zscore", "plage"),
35 36
     kcdf=c("Gaussian", "Poisson", "none"),
... ...
@@ -45,7 +46,8 @@ Estimates GSVA enrichment scores.
45 46
     tau=switch(method, gsva=1, ssgsea=0.25, NA),
46 47
     kernel=TRUE,
47 48
     ssgsea.norm=TRUE,
48
-    verbose=TRUE)
49
+    verbose=TRUE,
50
+    return.old.value=FALSE)
49 51
 \S4method{gsva}{matrix,GeneSetCollection}(expr, gset.idx.list, annotation,
50 52
     method=c("gsva", "ssgsea", "zscore", "plage"),
51 53
     kcdf=c("Gaussian", "Poisson", "none"),
... ...
@@ -61,7 +63,8 @@ Estimates GSVA enrichment scores.
61 63
     tau=switch(method, gsva=1, ssgsea=0.25, NA),
62 64
     kernel=TRUE,
63 65
     ssgsea.norm=TRUE,
64
-    verbose=TRUE)
66
+    verbose=TRUE,
67
+    return.old.value=FALSE)
65 68
 \S4method{gsva}{matrix,list}(expr, gset.idx.list, annotation,
66 69
     method=c("gsva", "ssgsea", "zscore", "plage"),
67 70
     kcdf=c("Gaussian", "Poisson", "none"),
... ...
@@ -77,7 +80,8 @@ Estimates GSVA enrichment scores.
77 80
     tau=switch(method, gsva=1, ssgsea=0.25, NA),
78 81
     kernel=TRUE,
79 82
     ssgsea.norm=TRUE,
80
-    verbose=TRUE)
83
+    verbose=TRUE,
84
+    return.old.value=FALSE)
81 85
 }
82 86
 \arguments{
83 87
   \item{expr}{Gene expression data which can be given either as an \code{ExpressionSet}
... ...
@@ -118,8 +122,10 @@ Estimates GSVA enrichment scores.
118 122
             enriched on either extreme (high or low) will be regarded as 'highly' activated.}
119 123
   \item{min.sz}{Minimum size of the resulting gene sets.}
120 124
   \item{max.sz}{Maximum size of the resulting gene sets.}
121
-  \item{no.bootstraps}{Number of bootstrap iterations to perform.}
122
-  \item{bootstrap.percent}{.632 is the ideal percent samples bootstrapped.}
125
+  \item{no.bootstraps}{Number of bootstrap iterations to perform. This argument has been deprecated and will
126
+                       be removed in the next release.}
127
+  \item{bootstrap.percent}{.632 is the ideal percent samples bootstrapped. This argument has been deprecated and
128
+                           will be removed in the next release.}
123 129
   \item{parallel.sz}{Number of processors to use when doing the calculations in parallel.
124 130
                      This requires to previously load either the \code{parallel} or the
125 131
                      \code{snow} library. If \code{parallel} is loaded and this argument
... ...
@@ -145,6 +151,11 @@ Estimates GSVA enrichment scores.
145 151
                      the minimum and the maximum, as described in their paper. When \code{ssgsea.norm=FALSE}
146 152
                      this last normalization step is skipped.}
147 153
   \item{verbose}{Gives information about each calculation step. Default: \code{FALSE}.}
154
+  \item{return.old.value}{Logical, set to \code{FALSE} (default) has no effect but when \code{return.old.value=TRUE},
155
+                          then the return value takes form of a \code{list} object as in previous versions of
156
+                          GSVA. This argument will be present only in this release for backward compability
157
+                          purposes during the deprecation of the arguments \code{no.bootstraps} and \code{bootstrap.percent}
158
+                          and will dissappear in the next release.}
148 159
 }
149 160
 
150 161
 \details{
... ...
@@ -216,7 +227,7 @@ fit <- eBayes(fit)
216 227
 topTable(fit, coef="sampleGroup2vs1")
217 228
 
218 229
 ## estimate GSVA enrichment scores for the three sets
219
-gsva_es <- gsva(y, geneSets, mx.diff=1)$es.obs
230
+gsva_es <- gsva(y, geneSets, mx.diff=1)
220 231
 
221 232
 ## fit the same linear model now to the GSVA enrichment scores
222 233
 fit <- lmFit(gsva_es, design)
... ...
@@ -172,8 +172,8 @@ X <- matrix(rnorm(p*n), nrow=p, dimnames=list(1:p, 1:n))
172 172
 dim(X)
173 173
 gs <- as.list(sample(min.sz:max.sz, size=nGS, replace=TRUE)) ## sample gene set sizes
174 174
 gs <- lapply(gs, function(n, p) sample(1:p, size=n, replace=FALSE), p) ## sample gene sets
175
-es.max <- gsva(X, gs, mx.diff=FALSE, verbose=FALSE, parallel.sz=1)$es.obs
176
-es.dif <- gsva(X, gs, mx.diff=TRUE, verbose=FALSE, parallel.sz=1)$es.obs
175
+es.max <- gsva(X, gs, mx.diff=FALSE, verbose=FALSE, parallel.sz=1)
176
+es.dif <- gsva(X, gs, mx.diff=TRUE, verbose=FALSE, parallel.sz=1)
177 177
 @
178 178
 
179 179
 \begin{center}
... ...
@@ -409,7 +409,7 @@ GSVA enrichment scores, we leave deliberately unchanged the default argument
409 409
 
410 410
 <<>>=
411 411
 cache(leukemia_es <- gsva(leukemia_filtered_eset, c2BroadSets,
412
-                           min.sz=10, max.sz=500, verbose=TRUE)$es.obs,
412
+                           min.sz=10, max.sz=500, verbose=TRUE),
413 413
                            dir=cacheDir, prefix=cachePrefix)
414 414
 @
415 415
 We test whether there is a difference between the GSVA enrichment scores from each
... ...
@@ -572,7 +572,7 @@ GSVA enrichment scores for the gene sets contained in \Robject{brainTxDbSets}
572 572
 are calculated, in this case using \Robject{mx.diff=FALSE},  as follows:
573 573
 
574 574
 <<>>=
575
-gbm_es <- gsva(gbm_eset, brainTxDbSets, mx.diff=FALSE, verbose=FALSE, parallel.sz=1)$es.obs
575
+gbm_es <- gsva(gbm_eset, brainTxDbSets, mx.diff=FALSE, verbose=FALSE, parallel.sz=1)
576 576
 @
577 577
 Figure \ref{gbmSignature} shows the GSVA enrichment scores obtained for the
578 578
 up-regulated gene sets across the samples of the four GBM subtypes. As expected,
... ...
@@ -664,7 +664,7 @@ runSim <- function(p, n, gs.sz, S2N, fracDEgs) {
664 664
   geneSets <- list(H1GeneSet=paste0("g", 1:(gs.sz)),
665 665
                    H0GeneSet=paste0("g", (gs.sz+1):(2*gs.sz)))
666 666
 
667
-  es.gsva <- gsva(M, geneSets, verbose=FALSE, parallel.sz=1)$es.obs
667
+  es.gsva <- gsva(M, geneSets, verbose=FALSE, parallel.sz=1)
668 668
   es.ss <- gsva(M, geneSets, method="ssgsea", verbose=FALSE, parallel.sz=1)
669 669
   es.z <- gsva(M, geneSets, method="zscore", verbose=FALSE, parallel.sz=1)
670 670
   es.plage <- gsva(M, geneSets, method="plage", verbose=FALSE, parallel.sz=1)
... ...
@@ -849,10 +849,10 @@ argument should remain unchanged.
849 849
 
850 850
 <<<>>=
851 851
 esmicro <- gsva(huangArrayRMAnoBatchCommon_eset, canonicalC2BroadSets, min.sz=5, max.sz=500,
852
-                mx.diff=TRUE, verbose=FALSE, parallel.sz=1)$es.obs
852
+                mx.diff=TRUE, verbose=FALSE, parallel.sz=1)
853 853
 dim(esmicro)
854 854
 esrnaseq <- gsva(pickrellCountsArgonneCQNcommon_eset, canonicalC2BroadSets, min.sz=5, max.sz=500,
855
-                 kcdf="Poisson", mx.diff=TRUE, verbose=FALSE, parallel.sz=1)$es.obs
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+                 kcdf="Poisson", mx.diff=TRUE, verbose=FALSE, parallel.sz=1)
856 856
 dim(esrnaseq)
857 857
 @
858 858
 To compare expression values from both technologies we are going to transform