context("ERASE-MAE")
test_that("ERASE-MAE works", {
load("testDataASE.rda")
load("testDataGWAS.rda")
load("testDataAnnSpi.rda")
#Intersection
asegwas <- getIntersection(dfAseData, dfGwasData)
expect_true(is.data.frame(asegwas))
asegwas$neglogpval <- -log10(asegwas$p)
aseann <- getIntersection(dfAseData,
dfAnnData,
snpAnnType="SPI",
snpAnnCol2Sel = "abs_dpsi_max_tissue")
expect_true(is.data.frame(aseann))
ase_gwas_ann <- getIntersectionMae(asegwas, aseann[ , c("cmp.col", "abs_dpsi_max_tissue")])
expect_true(is.data.frame(ase_gwas_ann))
#Randomization & transformation
asegwas_zscoreFile <- randomization(df_sigASE_SNPann = ase_gwas_ann[ ase_gwas_ann$min.FDR < 0.05, ],
df_nonASE_SNPann = ase_gwas_ann[ ase_gwas_ann$min.FDR >= 0.05, ],
colname_rankSNPann = "neglogpval",
colname_chk4distr = "avgReads",
outFilePrefix= "MAE_AseGwas",
nIterations = 5 ,
binwidth = 20,
mode="MAE",
seedValue=12345 )
aseann_zscoreFile <- randomization(df_sigASE_SNPann = ase_gwas_ann[ ase_gwas_ann$min.FDR < 0.05, ],
df_nonASE_SNPann = ase_gwas_ann[ ase_gwas_ann$min.FDR >= 0.05, ],
colname_rankSNPann = "abs_dpsi_max_tissue",
colname_chk4distr = "avgReads",
outFilePrefix= "MAE_AseAnn",
nIterations = 5 ,
binwidth = 20,
mode="MAE",
seedValue=67890 )
#Integration and p-value calculation
pval <- integrationPvalCalc(rdaSnpAnn1 = asegwas_zscoreFile,
rdaSnpAnn2 = aseann_zscoreFile,
outFilePrefix = "Inte_AseGwasAnn",
alphaVal = 0.5,
seedValue = 1234)
})
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