gfa_fit: Genetic Factor Analysis

In jean997/sumstatFactors: Genetic Factor Analysis (GFA)

Genetic Factor Analysis

Description

Genetic Factor Analysis

Usage

gfa_fit(
  Z_hat = NULL,
  N = NULL,
  N_case = NULL,
  pop_prev = NULL,
  B_hat = NULL,
  S = NULL,
  R = NULL,
  params = gfa_default_parameters(),
  method = c("fixed_factors", "random_effect"),
  mode = c("z-score", "b-std"),
  no_wrapup = FALSE,
  F_init = NULL,
  fix_F = FALSE
)

Arguments

Z_hat	A matrix of z-scores with rows for variants and columns for traits.
N	Vector of sample sizes length equal to number of traits. If not provided, N will default to the vector of 1's and the factor matrix will be returned on the "z-score scale".
B_hat	A matrix of GWAS effect estimates. B_hat is an alternative to Z_hat (only provide one of these). If using B_hat, you must also provide S.
S	If using B_hat, provide the corresponding matrix of standard errors.
R	Estimated residual correlation matrix. This can be produced for example using R_ldsc or R_pt.
params	List of parameters. For most users this can be left at the default values. See details.
method	Either "fixed_factors" or "random_effect". "random_effect" is experimental. See details.
mode	Either "z-score" or "b-std". See details.

Details

The method argument can be either fixed_factors or random_effect. These are different methods for fitting the GFA model. The random_effect method requires installing a fork of the flashier package using 'install_github("jean997/flashier", ref = "random-effect")'. Most users can stick with the fixed_factors method.

The mode option tells GFA to either fit using z-scores as the outcome and then convert the factor matrix to the standardized scale (mode = "z-score") or to fit directly using standardized effect estimates as the outcome (mode = "b-std"). These give very similar results and the z-score mode is faster and so recommended.

The params list includes the following elements which can be modified. Most users will not need to modify any of these, except possibly 'max_iter'.

kmax: Maximum number of factors. Defaults to twice the number of traits

cond_num: Maximum allowable condition number for R. Defaults to 1e5.

max_iter: Maximum number of iterations. Defaults to 1000.

extrapolate: Passed to flashier, defaults to TRUE

ebnm_fn_F and ebnm_fn_L: Prior families for factors and loadings. Defaults to point-normal.

init_fn: Flashier initialization function.

Value

A list with elements L_hat and F_hat for estimated variant-factor and factor-trait effects, gfa_pve which contains the proportion of heritability explained by each factor, and some other objects useful internally.

jean997/sumstatFactors documentation built on Jan. 24, 2025, 4:42 a.m.