scones.cv | R Documentation |
Finds the SNPs maximally associated with a phenotype while being connected in an underlying network. Select the hyperparameters by cross-validation.
scones.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"),
max_prop_snp = 0.5
)
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. |
max_prop_snp |
Maximum proportion of SNPs accepted in the model (between 0 and 1). Larger solutions will be discarded. |
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.
Azencott, C. A., Grimm, D., Sugiyama, M., Kawahara, Y., & Borgwardt, K. M. (2013). Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 29(13), 171-179. https://doi.org/10.1093/bioinformatics/btt238
gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
scones.cv(minigwas, gi)
scones.cv(minigwas, gi, score = "glm")
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