Mr.Mash Wrapper — mrmashWrapper • pecotmr

Compute weights with mr.mash using a precomputed prior grid and mixture prior.

Usage

mrmashWrapper(
  X,
  Y,
  V = NULL,
  sumstats = NULL,
  dataDrivenPriorMatrices = NULL,
  priorGrid = NULL,
  nthreads = 1,
  canonicalPriorMatrices = FALSE,
  standardize = FALSE,
  updateW0 = TRUE,
  w0Threshold = 1e-08,
  updateV = TRUE,
  updateVMethod = "full",
  bInitMethod = "enet",
  maxIter = 5000,
  tol = 0.01,
  verbose = FALSE,
  ...
)

Arguments

X: An n x p matrix of genotype data, where n is the total number of individuals and p is the number of SNPs.
Y: An n x r matrix of residual expression data, where n is the total number of individuals and r is the total number of conditions (tissue/cell-types).
V: Optional residual covariance matrix (conditions x conditions), or NULL to estimate it.
sumstats: Optional list of summary statistics for the RSS variant of mr.mash, or NULL for the individual-data variant.
dataDrivenPriorMatrices: A list of data-driven covariance matrices. Default is NULL.
priorGrid: A vector of scaling factors to be used in fitting the mr.mash model. Default is NULL.
nthreads: The number of threads to use for parallel computation. Default is 2.
canonicalPriorMatrices: A logical indicating whether to use canonical matrices as priors. Default is FALSE.
standardize: A logical indicating whether to standardize the input data. Default is FALSE.
updateW0: A logical indicating whether to update the prior mixture weights. Default is TRUE.
w0Threshold: The threshold for updating prior mixture weights. Default is 1e-8.
updateV: A logical indicating whether to update the residual covariance matrix. Default is TRUE.
updateVMethod: The method for updating the residual covariance matrix. Default is "full".
bInitMethod: The method for initializing the coefficient matrix. Default is "enet".
maxIter: The maximum number of iterations. Default is 5000.
tol: The tolerance for convergence. Default is 0.01.
verbose: A logical indicating whether to print verbose output. Default is FALSE.
...: Additional arguments to be passed to mr.mash.

Value

A mr.mash fit, stored as a list with some or all of the following elements:

mu1: A p x r matrix of posterior means for the regression coefficients.
S1: An r x r x p array of posterior covariances for the regression coefficients.
w1: A p x K matrix of posterior assignment probabilities to the mixture components.
V: An r x r residual covariance matrix.
w0: A K-vector with (updated, if update_w0=TRUE) prior mixture weights, each associated with the respective covariance matrix in S0.
S0: An r x r x K array of prior covariance matrices on the regression coefficients.
intercept: An r-vector containing the posterior mean estimate of the intercept.
fitted: An n x r matrix of fitted values.
G: An r x r covariance matrix of fitted values.
pve: An r-vector of proportion of variance explained by the covariates.
ELBO: The Evidence Lower Bound (ELBO) at the last iteration.
progress: A data frame including information regarding convergence criteria at each iteration.
converged: A logical indicating whether the optimization algorithm converged to a solution within the chosen tolerance level.
elapsed_time: The computation runtime for fitting mr.mash.
Y: An n x r matrix of responses at the last iteration (only relevant when missing values are present in the input Y).

Examples

data(multiTraitData)
res <- mrmashWrapper(
  X = multiTraitData$X[, 1:60], Y = multiTraitData$Y,
  dataDrivenPriorMatrices = multiTraitData$priorMatrices,
  canonicalPriorMatrices = TRUE
)