wmwTest: Wilcoxon-Mann-Whitney rank sum test for high-throughput...
In BioQC: Detect tissue heterogeneity in expression profiles with gene sets
Description
Usage
Arguments
Details
Value
Methods (by class)
Note
Author(s)
References
See Also
Examples
Description
We have implemented a highly efficient Wilcoxon-Mann-Whitney rank sum
test for high-throughput expression profiling data. For datasets with
more than 100 features (genes), the function can be more than 1,000
times faster than its R implementations (wilcox.test in
stats, or rankSumTestWithCorrelation in limma).
Usage
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wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
## S4 method for signature 'matrix,IndexList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'numeric,IndexList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'matrix,GmtList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'eSet,GmtList'
wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
## S4 method for signature 'eSet,numeric'
wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
## S4 method for signature 'eSet,logical'
wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
## S4 method for signature 'eSet,list'
wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
## S4 method for signature 'ANY,numeric'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'ANY,logical'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'ANY,list'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'matrix,SignedIndexList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'matrix,SignedGenesets'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'numeric,SignedIndexList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'eSet,SignedIndexList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'eSet,SignedGenesets'
wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
Arguments
x
A numeric matrix. All other data types (e.g. numeric vectors
or ExpressionSet objects) are coerced into matrix.
indexList
A list of integer indices (starting from 1) indicating
signature genes. Can be of length zero. Other data types (e.g. a list
of numeric or logical vectors, or a numeric or logical vector) are
coerced into such a list. See details below for a special case
using GMT files.
col
a string sometimes used with a eSet
valType
The value type to be returned, allowed values
include p.greater, p.less, abs.log10p.greater and
abs.log10p.less (one-sided tests),p.two.sided, and U
statistic, and their log10 transformation variants. See details below.
simplify
Logical. If not, the returning value is in matrix
format; if set to TRUE, the results are simplified into
vectors when possible (default).
Details
The basic application of the function is to test the enrichment of
gene sets in expression profiling data or differentially expressed
data (the matrix with feature/gene in rows and samples in columns).
A special case is when x is an eSet object
(e.g. ExpressionSet), and indexList is a list returned
from readGmt function. In this case, the only requirement is
that one column named GeneSymbol in the featureData
contain gene symbols used in the GMT file. The same applies to signed Gmt files. See the example below.
Besides the conventional value types such as ‘p.greater’,
‘p.less’, ‘p.two.sided’ , and ‘U’ (the U-statistic),
wmwTest (from version 0.99-1) provides further value types:
abs.log10p.greater and log10p.less perform log10
transformation on respective p-values and give the
transformed value a proper sign (positive for greater than, and
negative for less than); abs.log10p.two.sided transforms
two-sided p-values to non-negative values; and Q score
reports absolute log10-transformation of p-value of the
two-side variant, and gives a proper sign to it, depending on whether it is
rather greater than (positive) or less than (negative).
Value
A numeric matrix or vector containing the statistic.
Methods (by class)
-
x = matrix,indexList = IndexList: x is a matrix and indexList is a IndexList
-
x = numeric,indexList = IndexList: x is a numeric and indexList is a IndexList
-
x = matrix,indexList = GmtList: x is a matrix and indexList is a GmtList
-
x = eSet,indexList = GmtList: x is a eSet and indexList is a GmtList
-
x = eSet,indexList = numeric: x is a eSet and indexList is a numeric
-
x = eSet,indexList = logical: x is a eSet and indexList is a logical
-
x = eSet,indexList = list: x is a eSet and indexList is a list
-
x = ANY,indexList = numeric: x is ANY and indexList is a numeric
-
x = ANY,indexList = logical: x is ANY and indexList is a logical
-
x = ANY,indexList = list: x is ANY and indexList is a list
-
x = matrix,indexList = SignedIndexList: x is a matrix and indexList is a
SignedIndexList
-
x = matrix,indexList = SignedGenesets: x is a eSet and indexList is a
SignedIndexList
-
x = numeric,indexList = SignedIndexList: x is a numeric and indexList is a
SignedIndexList
-
x = eSet,indexList = SignedIndexList: x is a eSet and indexList is a
SignedIndexList
-
x = eSet,indexList = SignedGenesets: x is a eSet and indexList is a
SignedIndexList
Note
The function has been optimized for expression profiling data. It
avoids repetitive ranking of data as done by native R implementations
and uses efficient C code to increase the performance and control
memory use. Simulation studies using expression profiles of 22000
genes in 2000 samples and 200 gene sets suggested that the C
implementation can be >1000 times faster than the R
implementation. And it is possible to further accelerate by
parallel calling the function with mclapply in the multicore package.
Author(s)
Jitao David Zhang <jitao_david.zhang@roche.com>
References
Barry, W.T., Nobel, A.B., and Wright, F.A. (2008). A statistical framework for testing functional namespaces in microarray data. _Annals of Applied Statistics_ 2, 286-315.
Wu, D, and Smyth, GK (2012). Camera: a competitive gene set test
accounting for inter-gene correlation. _Nucleic Acids Research_ 40(17):e133
Zar, JH (1999). _Biostatistical Analysis 4th Edition_. Prentice-Hall International, Upper Saddle River, New Jersey.
See Also
codewilcox.test in the stats package, and rankSumTestWithCorrelation in
the limma package.
Examples
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## R-native data structures
set.seed(1887)
rd <- rnorm(1000)
rl <- sample(c(TRUE, FALSE), 1000, replace=TRUE)
wmwTest(rd, rl, valType="p.two.sided")
wmwTest(rd, which(rl), valType="p.two.sided")
rd1 <- rd + ifelse(rl, 0.5, 0)
wmwTest(rd1, rl, valType="p.greater")
wmwTest(rd1, rl, valType="U")
rd2 <- rd - ifelse(rl, 0.2, 0)
wmwTest(rd2, rl, valType="p.greater")
wmwTest(rd2, rl, valType="p.two.sided")
wmwTest(rd2, rl, valType="p.less")
## matrix forms
rmat <- matrix(c(rd, rd1, rd2), ncol=3, byrow=FALSE)
wmwTest(rmat, rl, valType="p.two.sided")
wmwTest(rmat, rl, valType="p.greater")
wmwTest(rmat, which(rl), valType="p.two.sided")
wmwTest(rmat, which(rl), valType="p.greater")
## other valTypes
wmwTest(rmat, which(rl), valType="U")
wmwTest(rmat, which(rl), valType="abs.log10p.greater")
wmwTest(rmat, which(rl), valType="log10p.less")
wmwTest(rmat, which(rl), valType="abs.log10p.two.sided")
wmwTest(rmat, which(rl), valType="Q")
## using ExpressionSet
data(sample.ExpressionSet)
testSet <- sample.ExpressionSet
fData(testSet)$GeneSymbol <- paste("GENE_",1:nrow(testSet), sep="")
mySig1 <- sample(c(TRUE, FALSE), nrow(testSet), prob=c(0.25, 0.75), replace=TRUE)
wmwTest(testSet, which(mySig1), valType="p.greater")
## using integer
exprs(testSet)[,1L] <- exprs(testSet)[,1L] + ifelse(mySig1, 50, 0)
wmwTest(testSet, which(mySig1), valType="p.greater")
## using lists
mySig2 <- sample(c(TRUE, FALSE), nrow(testSet), prob=c(0.6, 0.4), replace=TRUE)
wmwTest(testSet, list(first=mySig1, second=mySig2))
## using GMT file
gmt_file <- system.file("extdata/exp.tissuemark.affy.roche.symbols.gmt", package="BioQC")
gmt_list <- readGmt(gmt_file)
gss <- sample(unlist(sapply(gmt_list, function(x) x$genes)), 1000)
eset<-new("ExpressionSet",
exprs=matrix(rnorm(10000), nrow=1000L),
phenoData=new("AnnotatedDataFrame", data.frame(Sample=LETTERS[1:10])),
featureData=new("AnnotatedDataFrame",data.frame(GeneSymbol=gss)))
esetWmwRes <- wmwTest(eset ,gmt_list, valType="p.greater")
summary(esetWmwRes)
## using signed GMT file
signed_gmt_file <- system.file("extdata/test.gmt", package="BioQC")
signed_gmt <- readSignedGmt(signed_gmt_file)
esetSignedWmwRes <- wmwTest(eset, signed_gmt, valType="p.greater")
esetMat <- exprs(eset); rownames(esetMat) <- fData(eset)$GeneSymbol
esetSignedWmwRes2 <- wmwTest(esetMat, signed_gmt, valType="p.greater")
Example output
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
[1] 0.8535404
[1] 0.8535404
[1] 1.949087e-14
[1] 90452
[1] 0.9976222
[1] 0.004758899
[1] 0.002379449
[1] 8.535404e-01 3.898175e-14 4.758899e-03
[1] 4.267702e-01 1.949087e-14 9.976222e-01
[1] 8.535404e-01 3.898175e-14 4.758899e-03
[1] 4.267702e-01 1.949087e-14 9.976222e-01
[1] 124152 90452 137887
[1] 0.369805934 13.710168669 0.001033906
[1] -2.416062e-01 -8.437865e-15 -2.623524e+00
[1] 0.06877594 13.40913867 2.32249352
[1] 0.06877594 13.40913867 -2.32249352
A B C D E F G H
0.3968848 0.4750041 0.4090897 0.5238279 0.5757461 0.4790929 0.4343313 0.6157803
I J K L M N O P
0.6872272 0.5165239 0.4499611 0.5820556 0.4755881 0.4934181 0.4666891 0.4785086
Q R S T U V W X
0.6280457 0.2976157 0.3232340 0.6152199 0.6149396 0.3576255 0.3540736 0.4102298
Y Z
0.5086293 0.4288550
A B C D E F
4.427725e-06 4.750041e-01 4.090897e-01 5.238279e-01 5.757461e-01 4.790929e-01
G H I J K L
4.343313e-01 6.157803e-01 6.872272e-01 5.165239e-01 4.499611e-01 5.820556e-01
M N O P Q R
4.755881e-01 4.934181e-01 4.666891e-01 4.785086e-01 6.280457e-01 2.976157e-01
S T U V W X
3.232340e-01 6.152199e-01 6.149396e-01 3.576255e-01 3.540736e-01 4.102298e-01
Y Z
5.086293e-01 4.288550e-01
A B C D E F G
first 4.427725e-06 0.4750041 0.4090897 0.5238279 0.5757461 0.4790929 0.4343313
second 3.322462e-01 0.5830970 0.4508703 0.4503638 0.4788279 0.2795935 0.5079108
H I J K L M N
first 0.6157803 0.6872272 0.5165239 0.4499611 0.5820556 0.4755881 0.4934181
second 0.5554548 0.6009788 0.5637822 0.4024829 0.4129130 0.5557076 0.4844365
O P Q R S T U
first 0.4666891 0.4785086 0.6280457 0.2976157 0.3232340 0.6152199 0.6149396
second 0.5477364 0.5394920 0.4508703 0.5673067 0.4577155 0.4478326 0.4821416
V W X Y Z
first 0.3576255 0.3540736 0.4102298 0.5086293 0.4288550
second 0.3385415 0.6612245 0.5985069 0.6346372 0.3837895
1 2 3 4
Min. :0.02198 Min. :0.01316 Min. :0.005433 Min. :0.007333
1st Qu.:0.28609 1st Qu.:0.24582 1st Qu.:0.194880 1st Qu.:0.233636
Median :0.55866 Median :0.51048 Median :0.460976 Median :0.598480
Mean :0.55896 Mean :0.50834 Mean :0.470506 Mean :0.547411
3rd Qu.:0.79758 3rd Qu.:0.72584 3rd Qu.:0.706622 3rd Qu.:0.848243
Max. :1.00000 Max. :1.00000 Max. :1.000000 Max. :1.000000
5 6 7 8
Min. :0.002327 Min. :0.01225 Min. :0.00879 Min. :0.0002731
1st Qu.:0.262446 1st Qu.:0.27261 1st Qu.:0.23966 1st Qu.:0.2535412
Median :0.531550 Median :0.58203 Median :0.52827 Median :0.5691982
Mean :0.523777 Mean :0.55246 Mean :0.52780 Mean :0.5251984
3rd Qu.:0.773279 3rd Qu.:0.79958 3rd Qu.:0.80507 3rd Qu.:0.7977362
Max. :1.000000 Max. :1.00000 Max. :1.00000 Max. :1.0000000
9 10
Min. :0.001639 Min. :0.009709
1st Qu.:0.265712 1st Qu.:0.273843
Median :0.503677 Median :0.517334
Mean :0.513945 Mean :0.531123
3rd Qu.:0.738335 3rd Qu.:0.817494
Max. :1.000000 Max. :1.000000
BioQC documentation built on Nov. 8, 2020, 7:16 p.m.
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Description Usage Arguments Details Value Methods (by class) Note Author(s) References See Also Examples
Description
We have implemented a highly efficient Wilcoxon-Mann-Whitney rank sum
test for high-throughput expression profiling data. For datasets with
more than 100 features (genes), the function can be more than 1,000
times faster than its R implementations (wilcox.test in
stats, or rankSumTestWithCorrelation in limma).
Usage
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
## S4 method for signature 'matrix,IndexList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'numeric,IndexList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'matrix,GmtList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'eSet,GmtList'
wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
## S4 method for signature 'eSet,numeric'
wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
## S4 method for signature 'eSet,logical'
wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
## S4 method for signature 'eSet,list'
wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
## S4 method for signature 'ANY,numeric'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'ANY,logical'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'ANY,list'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'matrix,SignedIndexList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'matrix,SignedGenesets'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'numeric,SignedIndexList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'eSet,SignedIndexList'
wmwTest(x, indexList, valType, simplify = TRUE)
## S4 method for signature 'eSet,SignedGenesets'
wmwTest(
x,
indexList,
col = "GeneSymbol",
valType = c("p.greater", "p.less", "p.two.sided", "U", "abs.log10p.greater",
"log10p.less", "abs.log10p.two.sided", "Q"),
simplify = TRUE
)
|
Arguments
x |
A numeric matrix. All other data types (e.g. numeric vectors
or |
indexList |
A list of integer indices (starting from 1) indicating
signature genes. Can be of length zero. Other data types (e.g. a list
of numeric or logical vectors, or a numeric or logical vector) are
coerced into such a list. See |
col |
a string sometimes used with a |
valType |
The value type to be returned, allowed values
include |
simplify |
Logical. If not, the returning value is in matrix
format; if set to |
Details
The basic application of the function is to test the enrichment of gene sets in expression profiling data or differentially expressed data (the matrix with feature/gene in rows and samples in columns).
A special case is when x is an eSet object
(e.g. ExpressionSet), and indexList is a list returned
from readGmt function. In this case, the only requirement is
that one column named GeneSymbol in the featureData
contain gene symbols used in the GMT file. The same applies to signed Gmt files. See the example below.
Besides the conventional value types such as ‘p.greater’,
‘p.less’, ‘p.two.sided’ , and ‘U’ (the U-statistic),
wmwTest (from version 0.99-1) provides further value types:
abs.log10p.greater and log10p.less perform log10
transformation on respective p-values and give the
transformed value a proper sign (positive for greater than, and
negative for less than); abs.log10p.two.sided transforms
two-sided p-values to non-negative values; and Q score
reports absolute log10-transformation of p-value of the
two-side variant, and gives a proper sign to it, depending on whether it is
rather greater than (positive) or less than (negative).
Value
A numeric matrix or vector containing the statistic.
Methods (by class)
-
x = matrix,indexList = IndexList:xis amatrixandindexListis aIndexList -
x = numeric,indexList = IndexList:xis anumericandindexListis aIndexList -
x = matrix,indexList = GmtList:xis amatrixandindexListis aGmtList -
x = eSet,indexList = GmtList:xis aeSetandindexListis aGmtList -
x = eSet,indexList = numeric:xis aeSetandindexListis anumeric -
x = eSet,indexList = logical:xis aeSetandindexListis alogical -
x = eSet,indexList = list:xis aeSetandindexListis alist -
x = ANY,indexList = numeric:xisANYandindexListis anumeric -
x = ANY,indexList = logical:xisANYandindexListis alogical -
x = ANY,indexList = list:xisANYandindexListis alist -
x = matrix,indexList = SignedIndexList:xis amatrixandindexListis aSignedIndexList -
x = matrix,indexList = SignedGenesets:xis aeSetandindexListis aSignedIndexList -
x = numeric,indexList = SignedIndexList:xis anumericandindexListis aSignedIndexList -
x = eSet,indexList = SignedIndexList:xis aeSetandindexListis aSignedIndexList -
x = eSet,indexList = SignedGenesets:xis aeSetandindexListis aSignedIndexList
Note
The function has been optimized for expression profiling data. It
avoids repetitive ranking of data as done by native R implementations
and uses efficient C code to increase the performance and control
memory use. Simulation studies using expression profiles of 22000
genes in 2000 samples and 200 gene sets suggested that the C
implementation can be >1000 times faster than the R
implementation. And it is possible to further accelerate by
parallel calling the function with mclapply in the multicore package.
Author(s)
Jitao David Zhang <jitao_david.zhang@roche.com>
References
Barry, W.T., Nobel, A.B., and Wright, F.A. (2008). A statistical framework for testing functional namespaces in microarray data. _Annals of Applied Statistics_ 2, 286-315.
Wu, D, and Smyth, GK (2012). Camera: a competitive gene set test accounting for inter-gene correlation. _Nucleic Acids Research_ 40(17):e133
Zar, JH (1999). _Biostatistical Analysis 4th Edition_. Prentice-Hall International, Upper Saddle River, New Jersey.
See Also
codewilcox.test in the stats package, and rankSumTestWithCorrelation in
the limma package.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | ## R-native data structures
set.seed(1887)
rd <- rnorm(1000)
rl <- sample(c(TRUE, FALSE), 1000, replace=TRUE)
wmwTest(rd, rl, valType="p.two.sided")
wmwTest(rd, which(rl), valType="p.two.sided")
rd1 <- rd + ifelse(rl, 0.5, 0)
wmwTest(rd1, rl, valType="p.greater")
wmwTest(rd1, rl, valType="U")
rd2 <- rd - ifelse(rl, 0.2, 0)
wmwTest(rd2, rl, valType="p.greater")
wmwTest(rd2, rl, valType="p.two.sided")
wmwTest(rd2, rl, valType="p.less")
## matrix forms
rmat <- matrix(c(rd, rd1, rd2), ncol=3, byrow=FALSE)
wmwTest(rmat, rl, valType="p.two.sided")
wmwTest(rmat, rl, valType="p.greater")
wmwTest(rmat, which(rl), valType="p.two.sided")
wmwTest(rmat, which(rl), valType="p.greater")
## other valTypes
wmwTest(rmat, which(rl), valType="U")
wmwTest(rmat, which(rl), valType="abs.log10p.greater")
wmwTest(rmat, which(rl), valType="log10p.less")
wmwTest(rmat, which(rl), valType="abs.log10p.two.sided")
wmwTest(rmat, which(rl), valType="Q")
## using ExpressionSet
data(sample.ExpressionSet)
testSet <- sample.ExpressionSet
fData(testSet)$GeneSymbol <- paste("GENE_",1:nrow(testSet), sep="")
mySig1 <- sample(c(TRUE, FALSE), nrow(testSet), prob=c(0.25, 0.75), replace=TRUE)
wmwTest(testSet, which(mySig1), valType="p.greater")
## using integer
exprs(testSet)[,1L] <- exprs(testSet)[,1L] + ifelse(mySig1, 50, 0)
wmwTest(testSet, which(mySig1), valType="p.greater")
## using lists
mySig2 <- sample(c(TRUE, FALSE), nrow(testSet), prob=c(0.6, 0.4), replace=TRUE)
wmwTest(testSet, list(first=mySig1, second=mySig2))
## using GMT file
gmt_file <- system.file("extdata/exp.tissuemark.affy.roche.symbols.gmt", package="BioQC")
gmt_list <- readGmt(gmt_file)
gss <- sample(unlist(sapply(gmt_list, function(x) x$genes)), 1000)
eset<-new("ExpressionSet",
exprs=matrix(rnorm(10000), nrow=1000L),
phenoData=new("AnnotatedDataFrame", data.frame(Sample=LETTERS[1:10])),
featureData=new("AnnotatedDataFrame",data.frame(GeneSymbol=gss)))
esetWmwRes <- wmwTest(eset ,gmt_list, valType="p.greater")
summary(esetWmwRes)
## using signed GMT file
signed_gmt_file <- system.file("extdata/test.gmt", package="BioQC")
signed_gmt <- readSignedGmt(signed_gmt_file)
esetSignedWmwRes <- wmwTest(eset, signed_gmt, valType="p.greater")
esetMat <- exprs(eset); rownames(esetMat) <- fData(eset)$GeneSymbol
esetSignedWmwRes2 <- wmwTest(esetMat, signed_gmt, valType="p.greater")
|
Example output
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
[1] 0.8535404
[1] 0.8535404
[1] 1.949087e-14
[1] 90452
[1] 0.9976222
[1] 0.004758899
[1] 0.002379449
[1] 8.535404e-01 3.898175e-14 4.758899e-03
[1] 4.267702e-01 1.949087e-14 9.976222e-01
[1] 8.535404e-01 3.898175e-14 4.758899e-03
[1] 4.267702e-01 1.949087e-14 9.976222e-01
[1] 124152 90452 137887
[1] 0.369805934 13.710168669 0.001033906
[1] -2.416062e-01 -8.437865e-15 -2.623524e+00
[1] 0.06877594 13.40913867 2.32249352
[1] 0.06877594 13.40913867 -2.32249352
A B C D E F G H
0.3968848 0.4750041 0.4090897 0.5238279 0.5757461 0.4790929 0.4343313 0.6157803
I J K L M N O P
0.6872272 0.5165239 0.4499611 0.5820556 0.4755881 0.4934181 0.4666891 0.4785086
Q R S T U V W X
0.6280457 0.2976157 0.3232340 0.6152199 0.6149396 0.3576255 0.3540736 0.4102298
Y Z
0.5086293 0.4288550
A B C D E F
4.427725e-06 4.750041e-01 4.090897e-01 5.238279e-01 5.757461e-01 4.790929e-01
G H I J K L
4.343313e-01 6.157803e-01 6.872272e-01 5.165239e-01 4.499611e-01 5.820556e-01
M N O P Q R
4.755881e-01 4.934181e-01 4.666891e-01 4.785086e-01 6.280457e-01 2.976157e-01
S T U V W X
3.232340e-01 6.152199e-01 6.149396e-01 3.576255e-01 3.540736e-01 4.102298e-01
Y Z
5.086293e-01 4.288550e-01
A B C D E F G
first 4.427725e-06 0.4750041 0.4090897 0.5238279 0.5757461 0.4790929 0.4343313
second 3.322462e-01 0.5830970 0.4508703 0.4503638 0.4788279 0.2795935 0.5079108
H I J K L M N
first 0.6157803 0.6872272 0.5165239 0.4499611 0.5820556 0.4755881 0.4934181
second 0.5554548 0.6009788 0.5637822 0.4024829 0.4129130 0.5557076 0.4844365
O P Q R S T U
first 0.4666891 0.4785086 0.6280457 0.2976157 0.3232340 0.6152199 0.6149396
second 0.5477364 0.5394920 0.4508703 0.5673067 0.4577155 0.4478326 0.4821416
V W X Y Z
first 0.3576255 0.3540736 0.4102298 0.5086293 0.4288550
second 0.3385415 0.6612245 0.5985069 0.6346372 0.3837895
1 2 3 4
Min. :0.02198 Min. :0.01316 Min. :0.005433 Min. :0.007333
1st Qu.:0.28609 1st Qu.:0.24582 1st Qu.:0.194880 1st Qu.:0.233636
Median :0.55866 Median :0.51048 Median :0.460976 Median :0.598480
Mean :0.55896 Mean :0.50834 Mean :0.470506 Mean :0.547411
3rd Qu.:0.79758 3rd Qu.:0.72584 3rd Qu.:0.706622 3rd Qu.:0.848243
Max. :1.00000 Max. :1.00000 Max. :1.000000 Max. :1.000000
5 6 7 8
Min. :0.002327 Min. :0.01225 Min. :0.00879 Min. :0.0002731
1st Qu.:0.262446 1st Qu.:0.27261 1st Qu.:0.23966 1st Qu.:0.2535412
Median :0.531550 Median :0.58203 Median :0.52827 Median :0.5691982
Mean :0.523777 Mean :0.55246 Mean :0.52780 Mean :0.5251984
3rd Qu.:0.773279 3rd Qu.:0.79958 3rd Qu.:0.80507 3rd Qu.:0.7977362
Max. :1.000000 Max. :1.00000 Max. :1.00000 Max. :1.0000000
9 10
Min. :0.001639 Min. :0.009709
1st Qu.:0.265712 1st Qu.:0.273843
Median :0.503677 Median :0.517334
Mean :0.513945 Mean :0.531123
3rd Qu.:0.738335 3rd Qu.:0.817494
Max. :1.000000 Max. :1.000000
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