compute_isotwas: Compute isoTWAS model for a set of isoforms/transcripts

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

Compute isoTWAS model for a set of isoforms/transcripts

Description

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

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