SlaPMEG (version 1.0.1)

multslapmeg: Testing multiple pathways using SLaPMEG (shared latent process mixed effects model and Globaltest) for longitudinal Omics data

Description

Run slapmeg simultaneously for several pathways. For each pathway a p-value is calculated based on SLaPMEG prodcedure as in multslapmeg. Then the p-values are adjusted for multiple comparisons based on the selected procedure.

Usage

multslapmeg(pathlist, fixed, random, grouping, subject, method = "BH", data)

Arguments

pathlist

A list of pathways to be tested.

fixed

A one-sided linear formula object for specifying the fixed-effects in the linear mixed model at the latent process level that starts with the ~ sign.

random

A one-sided formula for the random-effects in the latent process mixed model and starts with the ~ sign. At least one random effect should be included. Covariates with a random-effect are separated by +.

grouping

name of the covariate representing the grouping structure.

subject

name of the covariate representing the repeated measures structure such as subject IDs.

method

Correction method for p-values, the default is "BH". For more methods see?p.adjust.

data

data frame containing the variables named in list of pathlist, fixed, random, grouping and subject.

Value

A datafram including the name of pathways and corresponding adjusted p-values.

References

Ebrahimpoor, Mitra, Pietro Spitali, Jelle J. Goeman, and Roula Tsonaka. "Pathway testing for longitudinal metabolomics." Statistics in Medicine (2021).

See Also

slapmeg, pairslapmeg, plotslapmeg

Examples

# NOT RUN {
# }
# NOT RUN {
# simulate data with 20 omics
testdata<-simslapmeg(nY=20, ntime=5, nsubj = 30, seed=123)
head(testdata)

# creat a list of 3 random pathways of different sizes
pathlist<-list(path1=sample(colnames(testdata)[-c(1:3)],5),
              path2=sample(colnames(testdata)[-c(1:3)],11),
              path3=sample(colnames(testdata)[-c(1:3)],9) )


#use mult slampmeg to get test for the differential expression of all pathways
#and get adjusted p-values
mfit<- multslapmeg(pathlist, ~time, ~1+time, grouping="group", subject="ID", data=testdata)
summary(mfit)
# }
# NOT RUN {
# }