alcohol {CVR}

Data sets for the alcohol dependence example

Description

A list of 3 data frames that contains the gene expression, DNA methylation and AUD (alcohol use disorder) of 46 human subjects. The data is already screened for quality control. For the raw data see the link below. For more details see the reference.

Usage

alcohol

Format

A list of 3 data frames:

gene

Human gene expression. A data frame of 46 rows and 300 columns.

meth

Human DNA methylation. A data frame of 46 rows and 500 columns.

disorder

Human AUD indicator. A data frame of 46 rows and 1 column. The first 23 subjects are AUDs and the others are matched controls.

Source

Alcohol dependence: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49393.

References

Chongliang Luo, Jin Liu, Dipak D. Dey and Kun Chen (2016) Canonical variate regression. Biostatistics, doi: 10.1093/biostatistics/kxw001.

Examples

############## Alcohol dependence example ######################
data(alcohol)
gene <- scale(as.matrix(alcohol$gene))
meth <- scale(as.matrix(alcohol$meth))
disorder <- as.matrix(alcohol$disorder)
alcohol.X <- list(X1 = gene, X2 = meth)
## Not run: 
  foldid <- c(rep(1:5, 4), c(3,4,5), rep(1:5, 4), c(1,2,5))
  ##  table(foldid, disorder)
  ## there maybe warnings due to the glm refitting with small sample size
  alcohol.cvr <- CVR(disorder, alcohol.X, rankseq = 2, etaseq = 0.02, 
                     family = "b", penalty = "L1", foldid = foldid )
  plot(alcohol.cvr)
  plot(gene %*% alcohol.cvr$solution$W[[1]][, 1], meth %*% alcohol.cvr$solution$W[[2]][, 1])
  cor(gene %*% alcohol.cvr$solution$W[[1]], meth %*% alcohol.cvr$solution$W[[2]])

## End(Not run)