sigmod.cv {martini} | R Documentation |
Finds the SNPs maximally associated with a phenotype while being connected in an underlying network. Select the hyperparameters by cross-validation.
sigmod.cv( gwas, net, covars = data.frame(), score = c("chi2", "glm", "r2"), criterion = c("stability", "bic", "aic", "aicc", "global_clustering", "local_clustering"), etas = numeric(), lambdas = numeric(), family = c("binomial", "poisson", "gaussian", "gamma"), link = c("logit", "log", "identity", "inverse") )
gwas |
A SnpMatrix object with the GWAS information. |
net |
An igraph network that connects the SNPs. |
covars |
A data frame with the covariates. It must contain a column 'sample' containing the sample IDs, and an additional columns for each covariate. |
score |
Association score to measure association between genotype and phenotype. Possible values: chi2 (default), glm. |
criterion |
String with the function to measure the quality of a split. |
etas |
Numeric vector with the etas to explore in the grid search. If ommited, it's automatically created based on the association scores. |
lambdas |
Numeric vector with the lambdas to explore in the grid search. If ommited, it's automatically created based on the association scores. |
family |
A string defining the generalized linear model family. This should match one of "binomial", "poisson", "gaussian" or "gamma". See snp.rhs.tests for details. |
link |
A string defining the link function for the GLM. This should match one of "logit", "log", "identity" or "inverse". See snp.rhs.tests for details. |
A copy of the SnpMatrix$map
data.frame
, with the
following additions:
c: contains the univariate association score for every single SNP.
selected: logical vector indicating if the SNP was selected by SConES or not.
module: integer with the number of the module the SNP belongs to.
Liu, Y., Brossard, M., Roqueiro, D., Margaritte-Jeannin, P., Sarnowski, C., Bouzigon, E., Demenais, F. (2017). SigMod: an exact and efficient method to identify a strongly interconnected disease-associated module in a gene network. Bioinformatics, 33(10), 1536–1544. https://doi.org/10.1093/bioinformatics/btx004
gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi) sigmod.cv(minigwas, gi) sigmod.cv(minigwas, gi, score = "glm")