stabilityBIG: Computes the area under the stability path for all covariates

In EpiSlim/epiGWAS: Robust Methods for Epistasis Detection

Description

This function implements the same model selection technique extensively described in stabilityGLM. The sole difference is the use of a different elastic net solver. In this function, we make use of biglasso. Thanks to its parallel backend, biglasso scales well to high-dimensional GWAS datasets. However, in our case, because of the use of additional backend files, a slight decrease in runtime is to be expected, compared with stabilityGLM.

Usage

stabilityBIG(
  X,
  Y,
  family = "gaussian",
  n_subsample = 20,
  n_lambda = 100,
  lambda_min_ratio = 0.01,
  eps = 1e-05,
  short = TRUE,
  ncores = 2
)

Arguments

X	design matrix formatted as a big.matrix object
Y	response vector
family	response type. Either 'gaussian' or 'binomial'
n_subsample	number of subsamples for stability selection
n_lambda	total number of lambda values
lambda_min_ratio	the minimum value of the regularization parameter lambda as a fraction of the maximum lambda, the first value for which the elastic net support is not empty.
eps	elastic net mixing parameter (see stabilityGLM for more details)
short	whether to compute the aucs only on the first half of the stability path. We observed better performance with thresholded paths
ncores	number of cores for the biglasso solver

Value

a vector grouping the aucs of all covariates within X

References

Slim, L., Chatelain, C., Azencott, C.-A., & Vert, J.-P. (2018). Novel Methods for Epistasis Detection in Genome-Wide Association Studies. BioRxiv.

Meinshausen, N., & Bühlmann, P. (2010). Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4), 417–473.

Haury, A. C., Mordelet, F., Vera-Licona, P., & Vert, J. P. (2012). TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Systems Biology, 6.

See Also

biglasso-package

Other support estimation functions: stabilityGLM()

Examples

n <- 100
p <- 25
X <- bigmemory::as.big.matrix(matrix(runif(n * p), ncol = p))
Y <- runif(n, min = 0, max = 1) < 0.5
aucBIG <- stabilityBIG(X, Y,
  family = "binomial", short = TRUE,
  ncores = 1, n_lambda = 200, n_subsample = 1
)

EpiSlim/epiGWAS documentation built on Nov. 19, 2019, 7:15 p.m.