# martini
[](https://travis-ci.org/hclimente/martini)
[](https://ci.appveyor.com/project/hclimente/martini)
[](https://codecov.io/gh/hclimente/martini)
[](https://bioconductor.org/packages/devel/bioc/html/martini.html)
`martini` is an R package to perform GWAS experiment that considers prior biological knowledge. This knowledge is modeled as a network of SNPs, were edges represent functional relationships between them (e.g. belonging to the same gene). Then, it looks for regions of the network associated with the phenotype using [SConES](https://academic.oup.com/bioinformatics/article/29/13/i171/198210).
# Installation
Install `martini` like any Bioconductor package:
``` r
install.packages("BiocManager")
BiocManager::install("martini")
```
# Usage
Running `martini` is a three step process:
``` r
library(martini)
# 1. Read GWAS data with read.pedfile (or load the example :) )
data(minigwas)
# 2. Create the SNP network: GS (structural information), GM (GS + gene
# annotation information) or GI (GM + protein-protein interaction information)
gs <- get_GS_network(minigwas)
# 3. Find connected, explanatory SNPs (cones)
cones <- search_cones(minigwas, gs)
# cones$selected informs about whether the SNP is selected as cones or not
head(cones)
# snp chr cm pos allele.1 allele.2 c selected module
# 3 1A1 1 0 10 A G 96.15385 TRUE 1
# 4 1A2 1 0 20 A G 96.15385 TRUE 1
# 5 1A3 1 0 30 A G 96.15385 TRUE 1
# 6 1A4 1 0 40 A G 96.15385 TRUE 1
# 7 1A5 1 0 50 A G 96.15385 TRUE 1
# 8 1A6 1 0 60 A G 96.15385 TRUE 1
```
Please, refer to the vignettes for more detailed usage examples. `martini` results can be further examined using the [blur](https://github.com/hclimente/blur) package.
