git-svn-id: file:///home/git/hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/GSVA@131287 bc3139a8-67e5-0310-9ffc-ced21a209358
... | ... |
@@ -1,5 +1,5 @@ |
1 | 1 |
Package: GSVA |
2 |
-Version: 1.25.3 |
|
2 |
+Version: 1.25.4 |
|
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"), |
... | ... |
@@ -8,6 +8,7 @@ setGeneric("gsva", function(expr, gset.idx.list, ...) standardGeneric("gsva")) |
8 | 8 |
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 |
+ kcdf=c("Gaussian", "Poisson", "none"), |
|
11 | 12 |
rnaseq=FALSE, |
12 | 13 |
abs.ranking=FALSE, |
13 | 14 |
min.sz=1, |
... | ... |
@@ -23,6 +24,13 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="list"), |
23 | 24 |
verbose=TRUE) |
24 | 25 |
{ |
25 | 26 |
method <- match.arg(method) |
27 |
+ kcdf <- match.arg(kcdf) |
|
28 |
+ |
|
29 |
+ if (!missing(rnaseq)) |
|
30 |
+ warning("The argument 'rnaseq' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.") |
|
31 |
+ |
|
32 |
+ if (!missing(kernel)) |
|
33 |
+ warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.") |
|
26 | 34 |
|
27 | 35 |
## filter out genes with constant expression values |
28 | 36 |
sdGenes <- Biobase::esApply(expr, 1, sd) |
... | ... |
@@ -52,7 +60,18 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="list"), |
52 | 60 |
min.sz=max(1, min.sz), |
53 | 61 |
max.sz=max.sz) |
54 | 62 |
|
55 |
- eSco <- .gsva(exprs(expr), mapped.gset.idx.list, method, rnaseq, abs.ranking, |
|
63 |
+ if (!missing(kcdf)) { |
|
64 |
+ if (kcdf == "Gaussian") { |
|
65 |
+ rnaseq <- FALSE |
|
66 |
+ kernel <- TRUE |
|
67 |
+ } else if (kcdf == "Poisson") { |
|
68 |
+ rnaseq <- TRUE |
|
69 |
+ kernel <- TRUE |
|
70 |
+ } else |
|
71 |
+ kernel <- FALSE |
|
72 |
+ } |
|
73 |
+ |
|
74 |
+ eSco <- .gsva(exprs(expr), mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking, |
|
56 | 75 |
no.bootstraps, bootstrap.percent, parallel.sz, parallel.type, |
57 | 76 |
mx.diff, tau, kernel, ssgsea.norm, verbose) |
58 | 77 |
|
... | ... |
@@ -70,6 +89,7 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="list"), |
70 | 89 |
setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="GeneSetCollection"), |
71 | 90 |
function(expr, gset.idx.list, annotation, |
72 | 91 |
method=c("gsva", "ssgsea", "zscore", "plage"), |
92 |
+ kcdf=c("Gaussian", "Poisson", "none"), |
|
73 | 93 |
rnaseq=FALSE, |
74 | 94 |
abs.ranking=FALSE, |
75 | 95 |
min.sz=1, |
... | ... |
@@ -85,6 +105,13 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="GeneSetCollecti |
85 | 105 |
verbose=TRUE) |
86 | 106 |
{ |
87 | 107 |
method <- match.arg(method) |
108 |
+ kcdf <- match.arg(kcdf) |
|
109 |
+ |
|
110 |
+ if (!missing(rnaseq)) |
|
111 |
+ warning("The argument 'rnaseq' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.") |
|
112 |
+ |
|
113 |
+ if (!missing(kernel)) |
|
114 |
+ warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.") |
|
88 | 115 |
|
89 | 116 |
## filter out genes with constant expression values |
90 | 117 |
sdGenes <- Biobase::esApply(expr, 1, sd) |
... | ... |
@@ -118,7 +145,18 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="GeneSetCollecti |
118 | 145 |
min.sz=max(1, min.sz), |
119 | 146 |
max.sz=max.sz) |
120 | 147 |
|
121 |
- eSco <- .gsva(exprs(expr), mapped.gset.idx.list, method, rnaseq, abs.ranking, |
|
148 |
+ if (!missing(kcdf)) { |
|
149 |
+ if (kcdf == "Gaussian") { |
|
150 |
+ rnaseq <- FALSE |
|
151 |
+ kernel <- TRUE |
|
152 |
+ } else if (kcdf == "Poisson") { |
|
153 |
+ rnaseq <- TRUE |
|
154 |
+ kernel <- TRUE |
|
155 |
+ } else |
|
156 |
+ kernel <- FALSE |
|
157 |
+ } |
|
158 |
+ |
|
159 |
+ eSco <- .gsva(exprs(expr), mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking, |
|
122 | 160 |
no.bootstraps, bootstrap.percent, parallel.sz, parallel.type, |
123 | 161 |
mx.diff, tau, kernel, ssgsea.norm, verbose) |
124 | 162 |
|
... | ... |
@@ -136,6 +174,7 @@ setMethod("gsva", signature(expr="ExpressionSet", gset.idx.list="GeneSetCollecti |
136 | 174 |
setMethod("gsva", signature(expr="matrix", gset.idx.list="GeneSetCollection"), |
137 | 175 |
function(expr, gset.idx.list, annotation, |
138 | 176 |
method=c("gsva", "ssgsea", "zscore", "plage"), |
177 |
+ kcdf=c("Gaussian", "Poisson", "none"), |
|
139 | 178 |
rnaseq=FALSE, |
140 | 179 |
abs.ranking=FALSE, |
141 | 180 |
min.sz=1, |
... | ... |
@@ -151,6 +190,13 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="GeneSetCollection"), |
151 | 190 |
verbose=TRUE) |
152 | 191 |
{ |
153 | 192 |
method <- match.arg(method) |
193 |
+ kcdf <- match.arg(kcdf) |
|
194 |
+ |
|
195 |
+ if (!missing(rnaseq)) |
|
196 |
+ warning("The argument 'rnaseq' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.") |
|
197 |
+ |
|
198 |
+ if (!missing(kernel)) |
|
199 |
+ warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.") |
|
154 | 200 |
|
155 | 201 |
## filter out genes with constant expression values |
156 | 202 |
sdGenes <- apply(expr, 1, sd) |
... | ... |
@@ -191,7 +237,18 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="GeneSetCollection"), |
191 | 237 |
min.sz=max(1, min.sz), |
192 | 238 |
max.sz=max.sz) |
193 | 239 |
|
194 |
- .gsva(expr, mapped.gset.idx.list, method, rnaseq, abs.ranking, |
|
240 |
+ if (!missing(kcdf)) { |
|
241 |
+ if (kcdf == "Gaussian") { |
|
242 |
+ rnaseq <- FALSE |
|
243 |
+ kernel <- TRUE |
|
244 |
+ } else if (kcdf == "Poisson") { |
|
245 |
+ rnaseq <- TRUE |
|
246 |
+ kernel <- TRUE |
|
247 |
+ } else |
|
248 |
+ kernel <- FALSE |
|
249 |
+ } |
|
250 |
+ |
|
251 |
+ .gsva(expr, mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking, |
|
195 | 252 |
no.bootstraps, bootstrap.percent, parallel.sz, parallel.type, |
196 | 253 |
mx.diff, tau, kernel, ssgsea.norm, verbose) |
197 | 254 |
}) |
... | ... |
@@ -199,6 +256,7 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="GeneSetCollection"), |
199 | 256 |
setMethod("gsva", signature(expr="matrix", gset.idx.list="list"), |
200 | 257 |
function(expr, gset.idx.list, annotation, |
201 | 258 |
method=c("gsva", "ssgsea", "zscore", "plage"), |
259 |
+ kcdf=c("Gaussian", "Poisson", "none"), |
|
202 | 260 |
rnaseq=FALSE, |
203 | 261 |
abs.ranking=FALSE, |
204 | 262 |
min.sz=1, |
... | ... |
@@ -214,6 +272,13 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="list"), |
214 | 272 |
verbose=TRUE) |
215 | 273 |
{ |
216 | 274 |
method <- match.arg(method) |
275 |
+ kcdf <- match.arg(kcdf) |
|
276 |
+ |
|
277 |
+ if (!missing(rnaseq)) |
|
278 |
+ warning("The argument 'rnaseq' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.") |
|
279 |
+ |
|
280 |
+ if (!missing(kernel)) |
|
281 |
+ warning("The argument 'kernel' is deprecated and will be removed in the next release of GSVA. Please use the 'kcdf' argument instead.") |
|
217 | 282 |
|
218 | 283 |
## filter out genes with constant expression values |
219 | 284 |
sdGenes <- apply(expr, 1, sd) |
... | ... |
@@ -242,13 +307,25 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="list"), |
242 | 307 |
min.sz=max(1, min.sz), |
243 | 308 |
max.sz=max.sz) |
244 | 309 |
|
245 |
- .gsva(expr, mapped.gset.idx.list, method, rnaseq, abs.ranking, no.bootstraps, |
|
310 |
+ if (!missing(kcdf)) { |
|
311 |
+ if (kcdf == "Gaussian") { |
|
312 |
+ rnaseq <- FALSE |
|
313 |
+ kernel <- TRUE |
|
314 |
+ } else if (kcdf == "Poisson") { |
|
315 |
+ rnaseq <- TRUE |
|
316 |
+ kernel <- TRUE |
|
317 |
+ } else |
|
318 |
+ kernel <- FALSE |
|
319 |
+ } |
|
320 |
+ |
|
321 |
+ .gsva(expr, mapped.gset.idx.list, method, kcdf, rnaseq, abs.ranking, no.bootstraps, |
|
246 | 322 |
bootstrap.percent, parallel.sz, parallel.type, |
247 | 323 |
mx.diff, tau, kernel, ssgsea.norm, verbose) |
248 | 324 |
}) |
249 | 325 |
|
250 | 326 |
.gsva <- function(expr, gset.idx.list, |
251 | 327 |
method=c("gsva", "ssgsea", "zscore", "plage"), |
328 |
+ kcdf=c("Gaussian", "Poisson", "none"), |
|
252 | 329 |
rnaseq=FALSE, |
253 | 330 |
abs.ranking=FALSE, |
254 | 331 |
no.bootstraps=0, |
... | ... |
@@ -261,6 +338,9 @@ setMethod("gsva", signature(expr="matrix", gset.idx.list="list"), |
261 | 338 |
ssgsea.norm=TRUE, |
262 | 339 |
verbose=TRUE) |
263 | 340 |
{ |
341 |
+ if (no.bootstraps > 0) |
|
342 |
+ warning("The 'no.bootstraps' and 'bootstrap.percent' arguments are experimental and will be deprecated and will dissapear in the following release of GSVA.") |
|
343 |
+ |
|
264 | 344 |
if(length(gset.idx.list) == 0){ |
265 | 345 |
stop("The gene set list is empty! Filter may be too stringent.") |
266 | 346 |
} |
... | ... |
@@ -98,16 +98,13 @@ argumentsDataInput <- function(id) { |
98 | 98 |
12, |
99 | 99 |
selectInput("method", "Choose method:", |
100 | 100 |
c("gsva","ssgsea","zscore","plage")), |
101 |
- radioButtons("rnaseq", "Rnaseq:", |
|
102 |
- c("False" = FALSE, |
|
103 |
- "True" = TRUE)), |
|
101 |
+ selectInput("kcdf", "Choose kcdf:", |
|
102 |
+ c("Gaussian","Poisson","none")), |
|
104 | 103 |
radioButtons("absRanking", "abs.ranking:", |
105 | 104 |
c("False" = FALSE, |
106 | 105 |
"True" = TRUE)), |
107 | 106 |
numericInput("minSz","min.sz:",value = 1), |
108 | 107 |
numericInput("maxSz","max.sz (Write 0 for infinite):",value = 0), |
109 |
- numericInput("noBootstraps","no.bootstraps:",value = 0), |
|
110 |
- numericInput("bootstrapPercent","bootstrap.percent:",value = .632), |
|
111 | 108 |
numericInput("parallelSz","parallel.sz:",value = 0), |
112 | 109 |
selectInput("parallelType", "parallel.type:", |
113 | 110 |
c("SOCK","MPI","NWS")), |
... | ... |
@@ -125,9 +122,6 @@ argumentsDataInput <- function(id) { |
125 | 122 |
conditionalPanel( |
126 | 123 |
condition = "input.method == 'zscore' || input.method == 'plage'" |
127 | 124 |
), |
128 |
- radioButtons("kernel", "kernel:", |
|
129 |
- c("True" = TRUE, |
|
130 |
- "False" = FALSE)), |
|
131 | 125 |
radioButtons("ssgseaNorm", "ssgsea.norm:", |
132 | 126 |
c("True" = TRUE, |
133 | 127 |
"False" = FALSE)), |
... | ... |
@@ -255,10 +249,10 @@ gsva_generation <- function(input, output, session, newY, genes,varMaxsz) { |
255 | 249 |
#GSVA Generation |
256 | 250 |
withProgress(message = 'Runing GSVA', value = 0, { |
257 | 251 |
incProgress(1, detail = "This may take a while...") |
258 |
- generated_gsva <<- gsva(newY, genes, method = input$method, rnaseq = as.logical(input$rnaseq), abs.ranking = as.logical(input$absRanking), |
|
259 |
- min.sz = input$minSz, max.sz = varMaxsz, no.bootstraps = input$noBootstraps, bootstrap.percent = input$bootstrapPercent, |
|
260 |
- parallel.sz = input$parallelSz, parallel.type = input$parallelType, mx.diff = as.logical(input$mxDiff), tau = selectedTau, kernel = as.logical(input$kernel), |
|
261 |
- ssgsea.norm = as.logical(input$ssgseaNorm), verbose = as.logical(input$verbose)) #Result asignation |
|
252 |
+ generated_gsva <<- gsva(newY, genes, method=input$method, kcdf=input$kcdf, abs.ranking=as.logical(input$absRanking), |
|
253 |
+ min.sz=input$minSz, max.sz=varMaxsz, parallel.sz=input$parallelSz, parallel.type=input$parallelType, |
|
254 |
+ mx.diff=as.logical(input$mxDiff), tau=selectedTau, ssgsea.norm=as.logical(input$ssgseaNorm), |
|
255 |
+ verbose=as.logical(input$verbose)) |
|
262 | 256 |
}) |
263 | 257 |
} |
264 | 258 |
|
... | ... |
@@ -16,6 +16,7 @@ Estimates GSVA enrichment scores. |
16 | 16 |
\usage{ |
17 | 17 |
\S4method{gsva}{ExpressionSet,list}(expr, gset.idx.list, annotation, |
18 | 18 |
method=c("gsva", "ssgsea", "zscore", "plage"), |
19 |
+ kcdf=c("Gaussian", "Poisson", "none"), |
|
19 | 20 |
rnaseq=FALSE, |
20 | 21 |
abs.ranking=FALSE, |
21 | 22 |
min.sz=1, |
... | ... |
@@ -31,6 +32,7 @@ Estimates GSVA enrichment scores. |
31 | 32 |
verbose=TRUE) |
32 | 33 |
\S4method{gsva}{ExpressionSet,GeneSetCollection}(expr, gset.idx.list, annotation, |
33 | 34 |
method=c("gsva", "ssgsea", "zscore", "plage"), |
35 |
+ kcdf=c("Gaussian", "Poisson", "none"), |
|
34 | 36 |
rnaseq=FALSE, |
35 | 37 |
abs.ranking=FALSE, |
36 | 38 |
min.sz=1, |
... | ... |
@@ -46,6 +48,7 @@ Estimates GSVA enrichment scores. |
46 | 48 |
verbose=TRUE) |
47 | 49 |
\S4method{gsva}{matrix,GeneSetCollection}(expr, gset.idx.list, annotation, |
48 | 50 |
method=c("gsva", "ssgsea", "zscore", "plage"), |
51 |
+ kcdf=c("Gaussian", "Poisson", "none"), |
|
49 | 52 |
rnaseq=FALSE, |
50 | 53 |
abs.ranking=FALSE, |
51 | 54 |
min.sz=1, |
... | ... |
@@ -61,6 +64,7 @@ Estimates GSVA enrichment scores. |
61 | 64 |
verbose=TRUE) |
62 | 65 |
\S4method{gsva}{matrix,list}(expr, gset.idx.list, annotation, |
63 | 66 |
method=c("gsva", "ssgsea", "zscore", "plage"), |
67 |
+ kcdf=c("Gaussian", "Poisson", "none"), |
|
64 | 68 |
rnaseq=FALSE, |
65 | 69 |
abs.ranking=FALSE, |
66 | 70 |
min.sz=1, |
... | ... |
@@ -97,12 +101,15 @@ Estimates GSVA enrichment scores. |
97 | 101 |
while in the case of \code{plage} they are used to calculate the singular value decomposition |
98 | 102 |
(SVD) over the genes in the gene set and use the coefficients of the first right-singular vector |
99 | 103 |
as pathway activity profile.} |
100 |
- \item{rnaseq}{Logical flag set by default to \code{rnaseq=FALSE} to inform whether the input gene |
|
101 |
- expression data are continues values, such as fluorescent units in logarithmic scale |
|
102 |
- from microarray experiments or some other kind of continuous value derived from |
|
103 |
- RNA-seq counts such as log-CPMs, log-RPKMs or log-TPMs. This flag should be set to |
|
104 |
- \code{rnaseq=TRUE} only when the values of the input gene expression data are integer |
|
105 |
- counts.} |
|
104 |
+ \item{kcdf}{Character string denoting the kernel to use during the non-parametric estimation of the |
|
105 |
+ cumulative distribution function of expression levels across samples when \code{method="gsva"}. |
|
106 |
+ By default, \code{kcdf="Gaussian"} which is suitable when input expression values are continuous, |
|
107 |
+ such as microarray fluorescent units in logarithmic scale, RNA-seq log-CPMs, log-RPKMs or log-TPMs. |
|
108 |
+ When input expression values are integer counts, such as those derived from RNA-seq experiments, |
|
109 |
+ then this argument should be set to \code{kcdf="Poisson"}. This argument supersedes arguments |
|
110 |
+ \code{rnaseq} and \code{kernel}, which are deprecated and will be removed in the next release.} |
|
111 |
+ \item{rnaseq}{This argument has been deprecated and will be removed in the next release. Please use the |
|
112 |
+ argument \code{kcdf} instead.} |
|
106 | 113 |
\item{abs.ranking}{Flag used only when \code{mx.diff=TRUE}. When \code{abs.ranking=FALSE} (default) |
107 | 114 |
a modified Kuiper statistic is used to calculate enrichment scores, taking the magnitude |
108 | 115 |
difference between the largest positive and negative random walk deviations. When |
... | ... |
@@ -131,10 +138,8 @@ Estimates GSVA enrichment scores. |
131 | 138 |
(\enc{H�nzelmann}{Hanzelmann} et al., 2013) and the \code{ssgsea} (Barbie et al., 2009) methods. By default, |
132 | 139 |
this \code{tau=1} when \code{method="gsva"} and \code{tau=0.25} when \code{method="ssgsea"} just |
133 | 140 |
as specified by Barbie et al. (2009) where this parameter is called \code{alpha}.} |
134 |
- \item{kernel}{Logical, set to \code{TRUE} when the GSVA method employes a kernel non-parametric |
|
135 |
- estimation of the empirical cumulative distribution function (default) and \code{FALSE} |
|
136 |
- when this function is directly estimated from the observed data. This last option is |
|
137 |
- justified in the limit of the size of the sample by the so-called Glivenko-Cantelli theorem.} |
|
141 |
+ \item{kernel}{This argument has been deprecated and will be removed in the next release. Please use the |
|
142 |
+ argument \code{kcdf} instead.} |
|
138 | 143 |
\item{ssgsea.norm}{Logical, set to \code{TRUE} (default) with \code{method="ssgsea"} runs the SSGSEA method |
139 | 144 |
from Barbie et al. (2009) normalizing the scores by the absolute difference between |
140 | 145 |
the minimum and the maximum, as described in their paper. When \code{ssgsea.norm=FALSE} |
... | ... |
@@ -154,16 +159,6 @@ identifiers in the input expression data leading to a filtered collection of |
154 | 159 |
gene sets. This collection can be further filtered to require a minimun and/or |
155 | 160 |
maximum size of the gene sets for which we want to calculate GSVA enrichment |
156 | 161 |
scores, by using the arguments \code{min.sz} and \code{max.sz}. |
157 |
- |
|
158 |
-The name of the argument \code{rnaseq} can be misleading. When set to \code{rnaseq=FALSE}, the |
|
159 |
-nonparametric estimation of the cumulative density function of the expression profile of |
|
160 |
-each gene across samples is done using Gaussian kernels suited for continuous values. These were |
|
161 |
-initially thought to be only microarray fluorescent units in logarithmic scale but nowadays these |
|
162 |
-may also correspond to continuous values derived from RNA-seq experiments such as log-CPMs, |
|
163 |
-log-RPKMs or log-TPMs. When \code{rnaseq=TRUE}, Poisson kernels are used instead and therefore, |
|
164 |
-this option is only suitable when the input gene expression matrix is formed by integer counts. |
|
165 |
-This implies that \code{rnaseq=FALSE} may also be used even when the expression data comes from |
|
166 |
-a RNA-seq experiment. The name of this argument may change in the future to avoid this confusion. |
|
167 | 162 |
} |
168 | 163 |
\value{ |
169 | 164 |
A gene-set by sample matrix of GSVA enrichment scores. |
... | ... |
@@ -841,15 +841,18 @@ canonicalC2BroadSets <- GeneSetCollection(c(canonicalC2BroadSets, MSY, XiE)) |
841 | 841 |
canonicalC2BroadSets |
842 | 842 |
@ |
843 | 843 |
We calculate now GSVA enrichment scores for these gene sets using first the microarray |
844 |
-data and then the RNA-seq data. Note that the only requirement to do the latter is to |
|
845 |
-set the argument \Robject{rnaseq=TRUE} which is \Robject{FALSE} by default. |
|
844 |
+data and then the RNA-seq integer count data. Note that the only requirement to do the |
|
845 |
+latter is to set the argument \Robject{kcdf="Poisson"} which is \Robject{"Gaussian"} by |
|
846 |
+default. Note, however, that if our RNA-seq derived expression levels would be continous, |
|
847 |
+such as log-CPMs, log-RPKMs or log-TPMs, the the default value of the \Robject{kcdf} |
|
848 |
+argument should remain unchanged. |
|
846 | 849 |
|
847 | 850 |
<<<>>= |
848 | 851 |
esmicro <- gsva(huangArrayRMAnoBatchCommon_eset, canonicalC2BroadSets, min.sz=5, max.sz=500, |
849 |
- mx.diff=TRUE, verbose=FALSE, rnaseq=FALSE, parallel.sz=1)$es.obs |
|
852 |
+ mx.diff=TRUE, verbose=FALSE, parallel.sz=1)$es.obs |
|
850 | 853 |
dim(esmicro) |
851 | 854 |
esrnaseq <- gsva(pickrellCountsArgonneCQNcommon_eset, canonicalC2BroadSets, min.sz=5, max.sz=500, |
852 |
- mx.diff=TRUE, verbose=FALSE, rnaseq=TRUE, parallel.sz=1)$es.obs |
|
855 |
+ kcdf="Poisson", mx.diff=TRUE, verbose=FALSE, parallel.sz=1)$es.obs |
|
853 | 856 |
dim(esrnaseq) |
854 | 857 |
@ |
855 | 858 |
To compare expression values from both technologies we are going to transform |