Compute isoTWAS model for a set of isoforms/transcripts

The function runs MVR models for a set of transcripts and outputs the best model

Usage

compute_isotwas(
  X,
  Y,
  gene_exp = NULL,
  Y.rep,
  R,
  id,
  omega_est = "replicates",
  omega_nlambda = 10,
  method = c("mrce_lasso", "curds_whey", "multi_enet", "joinet", "spls", "finemap",
    "univariate"),
  predict_nlambda = 50,
  family = "gaussian",
  scale = F,
  alpha = 0.5,
  nfolds = 5,
  verbose = F,
  par = F,
  n.cores = NULL,
  tx_names = NULL,
  seed = NULL,
  run_all = T,
  return_all = F,
  tol.in = 0.001,
  maxit.in = 1000,
  coverage = 0.9
)

Arguments

X: matrix, design matrix of SNP dosages
Y: matrix, matrix of G isoform expression across columns
gene_exp: vector, vector of total gene expression
Y.rep: matrix, matrix of G isoform expression with replicates
R: int, number of replicates
id: vector, vector of sample ids showing rep to id
omega_est: character, 'replicates' or 'mean' to use Y.rep or Y
omega_nlambda: int, number of omegas to generate
method: character, vector of methods to use
predict_nlambda: int, number of lambdas in MRCE
family: character, glmnet family
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 - order of columns of Y
seed: int, random seed
run_all: logical, run all methods
return_all: logical, return R2 for all models?
tol.in: numeric, tolerance for objective difference
maxit.in: int, maximum number of interactions
coverage: numeric, coverage of cred set for finemap and regress

Value

optimal isoTWAS model