tests/testthat/testGSA.r
In billyhw/GSALightning: Fast Permutation-based Gene Set Analysis
# to install the testing branch of GSA-Lightning
# library(githubinstall)
# gh_install_packages("GSALighting", ref = "testing")
# to check coverage use package_coverage() in package "covr"
library(GSA)
set.seed(100)
x <- matrix(rnorm(1000*20),ncol=20)
rownames(x) <- paste("g",1:1000,sep="")
dd <- sample(1:1000,size=100)
genenames=paste("g",1:1000,sep="")
y <- factor(c(rep(0,10),rep(1,10)))
yy <- factor(c(rep(1,10),rep(2,10)))
#create some random gene sets
genesets=vector("list",50)
for(i in 1:50){
genesets[[i]]=paste("g",sample(1:1000,size=30),sep="")
}
names(genesets)=paste("set",as.character(1:50),sep="")
test_that("Unpaired Test: Concordance of Gene set Statistics and p-values with GSA", {
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
method = 'mean', restandardize = TRUE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
method = 'mean', restandardize = TRUE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = TRUE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,2], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,2], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
method = 'mean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
method = 'mean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,2], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,2], GSAResult$pvalues.hi), 0.995)
# GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
# method = 'absmean', restandardize = TRUE)
# GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
# method = 'absmean', restandardize = TRUE)
# GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "absmean",
# resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = TRUE)
# expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
# expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
# expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
# expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
method = 'absmean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
method = 'absmean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "absmean",
resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
method = 'maxmean', restandardize = TRUE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
method = 'maxmean', restandardize = TRUE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "maxmean",
resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = TRUE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,2], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,2], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
method = 'maxmean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
method = 'maxmean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "maxmean",
resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,2], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,2], GSAResult$pvalues.hi), 0.995)
})
# classes for paired-data
y <- yy <- c(1:10, -(1:10))
test_that("Paired-Test: Concordance of Gene set Statistics and p-values with GSA", {
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
method = 'mean', restandardize = TRUE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
method = 'mean', restandardize = TRUE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = TRUE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
method = 'mean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
method = 'mean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
# GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
# method = 'absmean', restandardize = TRUE)
# GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
# method = 'absmean', restandardize = TRUE)
# GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "absmean",
# resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = TRUE)
# expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
# expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
# expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
# expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
method = 'absmean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
method = 'absmean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "absmean",
resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
method = 'maxmean', restandardize = TRUE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
method = 'maxmean', restandardize = TRUE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "maxmean",
resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = TRUE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
method = 'maxmean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
method = 'maxmean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "maxmean",
resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
})
y <- factor(c(rep(0,7),rep(1,7),rep(2,6)))
yy <- factor(c(rep(1,7),rep(2,7),rep(3,6)))
test_that("Multi-Test: Concordance of Gene set Statistics and p-values with GSA", {
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'multi',
method = 'mean', restandardize = TRUE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'multi',
method = 'mean', restandardize = TRUE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Multiclass", s0 = 0, s0.perc=-1, restand = TRUE)
expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'multi',
method = 'mean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'multi',
method = 'mean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Multiclass", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
})
billyhw/GSALightning documentation built on May 12, 2019, 9:22 p.m.
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# to install the testing branch of GSA-Lightning
# library(githubinstall)
# gh_install_packages("GSALighting", ref = "testing")
# to check coverage use package_coverage() in package "covr"
library(GSA)
set.seed(100)
x <- matrix(rnorm(1000*20),ncol=20)
rownames(x) <- paste("g",1:1000,sep="")
dd <- sample(1:1000,size=100)
genenames=paste("g",1:1000,sep="")
y <- factor(c(rep(0,10),rep(1,10)))
yy <- factor(c(rep(1,10),rep(2,10)))
#create some random gene sets
genesets=vector("list",50)
for(i in 1:50){
genesets[[i]]=paste("g",sample(1:1000,size=30),sep="")
}
names(genesets)=paste("set",as.character(1:50),sep="")
test_that("Unpaired Test: Concordance of Gene set Statistics and p-values with GSA", {
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
method = 'mean', restandardize = TRUE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
method = 'mean', restandardize = TRUE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = TRUE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,2], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,2], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
method = 'mean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
method = 'mean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,2], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,2], GSAResult$pvalues.hi), 0.995)
# GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
# method = 'absmean', restandardize = TRUE)
# GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
# method = 'absmean', restandardize = TRUE)
# GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "absmean",
# resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = TRUE)
# expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
# expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
# expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
# expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
method = 'absmean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
method = 'absmean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "absmean",
resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
method = 'maxmean', restandardize = TRUE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
method = 'maxmean', restandardize = TRUE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "maxmean",
resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = TRUE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,2], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,2], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'unpaired',
method = 'maxmean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'unpaired',
method = 'maxmean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "maxmean",
resp.type = "Two class unpaired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,2], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,2], GSAResult$pvalues.hi), 0.995)
})
# classes for paired-data
y <- yy <- c(1:10, -(1:10))
test_that("Paired-Test: Concordance of Gene set Statistics and p-values with GSA", {
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
method = 'mean', restandardize = TRUE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
method = 'mean', restandardize = TRUE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = TRUE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
method = 'mean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
method = 'mean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
# GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
# method = 'absmean', restandardize = TRUE)
# GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
# method = 'absmean', restandardize = TRUE)
# GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "absmean",
# resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = TRUE)
# expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
# expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
# expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
# expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
method = 'absmean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
method = 'absmean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "absmean",
resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
method = 'maxmean', restandardize = TRUE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
method = 'maxmean', restandardize = TRUE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "maxmean",
resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = TRUE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'paired',
method = 'maxmean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'paired',
method = 'maxmean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "maxmean",
resp.type = "Two class paired", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,5], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
})
y <- factor(c(rep(0,7),rep(1,7),rep(2,6)))
yy <- factor(c(rep(1,7),rep(2,7),rep(3,6)))
test_that("Multi-Test: Concordance of Gene set Statistics and p-values with GSA", {
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'multi',
method = 'mean', restandardize = TRUE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'multi',
method = 'mean', restandardize = TRUE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Multiclass", s0 = 0, s0.perc=-1, restand = TRUE)
expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
GSALightResult <- GSALight(x, y, genesets, nperm = 1000, tests = 'multi',
method = 'mean', restandardize = FALSE)
GSALightResult2 <- GSALight(x, y, genesets, nperm = 1000, npermBreaks = 19, tests = 'multi',
method = 'mean', restandardize = FALSE)
GSAResult <- GSA(x, yy, genesets, genenames, nperms=1000, method = "mean",
resp.type = "Multiclass", s0 = 0, s0.perc=-1, restand = FALSE)
expect_equivalent(GSALightResult[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult[,1], GSAResult$pvalues.hi), 0.995)
expect_equivalent(GSALightResult2[,3], GSAResult$GSA.scores)
expect_gt(cor(GSALightResult2[,1], GSAResult$pvalues.hi), 0.995)
})
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