scones.cv {martini} | R Documentation

## Find connected explanatory SNPs.

### Description

Finds the SNPs maximally associated with a phenotype while being connected in an underlying network. Select the hyperparameters by cross-validation.

### Usage

    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")
    )

### Arguments

`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.

### Value

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.

### References

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

### Examples

    gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
    scones.cv(minigwas, gi)
    scones.cv(minigwas, gi, score = "glm")

[Package martini version 1.14.0 Index]