multivariate_elasticnet: Multivariate elastic net on all columns with row-wise penalty

In bhattacharya-a-bt/isoTWAS: Isoform-level transcriptome-wide association studies

Multivariate elastic net on all columns with row-wise penalty

Description

The function trains multivariate elastic net models for all isoform transcripts jointly

Usage

multivariate_elasticnet(
  X,
  Y,
  Omega,
  scale = F,
  alpha = 0.5,
  nfolds = 5,
  verbose = T,
  par = F,
  n.cores = NULL,
  tx_names = NULL,
  seed
)

Arguments

X	matrix, design matrix of SNP dosages
Y	matrix, matrix of G isoform expression across columns
Omega	matrix, precision matrix of Y
scale	logical, T/F to scale Y by Omega
alpha	numeric, elastic net mixing parameter
nfolds	int, number of CV folds
verbose	logical
par	logical, uses mclapply to parallelize model fit
n.cores	int, number of parallel cores
tx_names	vector, character vector of tx names in order of columns of Y
seed	int, random seed

Value

data frame of elastic net, lasso, and LMM based predictions

bhattacharya-a-bt/isoTWAS documentation built on Jan. 9, 2025, 10:25 p.m.