Bayesian Guided Sparse Factor Analysis on Perturbed Gene Expression Matrix

Performs GSFA on given gene expression matrix and matching perturbation information using Gibbs sampling.

Usage

fit_gsfa_multivar(
  Y,
  G,
  K,
  fit0,
  use_neg_control = FALSE,
  neg_control_index = NULL,
  prior_type = c("mixture_normal", "spike_slab"),
  init.method = c("svd", "random"),
  prior_w_s = 50,
  prior_w_r = 0.2,
  prior_beta_s = 20,
  prior_beta_r = 0.2,
  niter = 500,
  used_niter = floor(niter/2),
  lfsr_niter = used_niter,
  verbose = TRUE,
  return_samples = TRUE
)

Arguments

Y

A sample by gene numeric matrix that stores normalized gene expression values; is.matrix(Y) should be TRUE;

G

Either a numeric vector or a sample by perturbation numeric matrix that stores sample-level perturbation information; length or nrow of G should be the same as nrow(Y);

K

Number of factors to use in the model; only one of K and fit0 is needed;

fit0

A list of class 'gsfa_fit' that is obtained from a previous fit_gsfa_multivar run, so that more iterations of Gibbs sampling can continue from the last updates in it;

use_neg_control

Boolean indicator of whether estimated effects should be calibrated using negative control effects as baseline instead of 0;

neg_control_index

Required when use_neg_control is TRUE, an integer from 1 to the total number of perturbation groups in G;

prior_type

Type of sparse prior used on gene weights, can be "mixture_normal" or "spike_slab", "mixture_normal" sometimes works better in inducing sparsity;

init.method

Method to initialize the factors, can be one of "svd" (truncated SVD on Y) or "random";

prior_w_s, prior_w_r

prior parameters (\(s_{w}\) and \(r_{w}\)) of the gene loading on the factors;

prior_beta_s, prior_beta_r

prior parameters (\(s_{b}\) and \(r_{b}\)) of the effects of perturbations on the factors;

niter

Total number of Gibbs sampling iterations;

used_niter

Number of iterations (counting from the last iteration) from which the posterior means of parameters are to be computed;

lfsr_niter

Number of iterations (counting from the last iteration) of posterior samples to use for the computation of LFSR;

return_samples

Boolean indicator of whether all posterior samples throughout Gibbs sampling should be returned;

Value

A list of class 'gsfa_fit' which stores the Gibbs sampling updates and posterior mean estimates, and the prior parameters used during the inference.

Details

Uses functions implemented in Rcpp from GSFA_gibbs_inference.cpp. For details about the GSFA model and prior specification, please see the GSFA paper and Supplementary Notes (Section 1).

Examples

if (FALSE) {
fit0 <- fit_gsfa_multivar(Y, G, 10, init.method = "svd", niter = 500, used_niter = 200)
fit1 <- fit_gsfa_multivar(Y, G, fit0 = fit0, niter = 500, used_niter = 200)
}