GPATree: Fit GPA-Tree model
In asthakhatiwada/GPATree: A package to implement the GPA-Tree method

Description Usage Arguments Details Value Author(s) Examples

This function will implement the GPA-Tree approach for integrative analysis of GWAS and functional annotation data.

1	GPATree(gwasPval, annMat, initAlpha = 0.1, cpTry = 0.001)

`gwasPval`	A matrix of M X 1 dimension, where M is the number of SNPs. The matrix includes the GWAS association p-values for the phenotype. P-values must be between 0 and 1.
`annMat`	A matrix of binary annotations, where rows and columns correspond to SNPs and annotations, respectively.
`initAlpha`	Initial value for alpha estimate. Default is 0.1.
`cpTry`	Complexity parameter (cp) value to be used. cpTry can be between 0 and 1 or NULL. Default is 0.001. When cpTry is NULL, GPATree will select the optimal cp to be used.

The GPATree() function fits the GPATree model. It requires to provide GWAS p-value to gwasPval and binary annotation data to annMat. It is assumed that number of rows of matrix in gwasPval and annMat are equal and correspond to the same SNP.

The assoc() function implements association mapping.

The plot() function takes in an object of class GPATree and will plot the functional annotation tree from the GPATree model.

The leaf() function takes in an object of class GPATree and will provide information regarding the functional annotations that are enriched (1) or not enriched (0) for SNPs in any leaf of the GPATree model plot.

The prune() function takes in an object of class GPATree and a cp parameter and will prune the GPATree model result. This function can be useful when the tree obtained from GPATree model is huge.

The ShinyGPATree app provides visualization of the GPA-Tree model, identifies risk-associated SNPs, and characterizes the combinations of functional annotations that can describe the risk-associated SNPs. The app can also be utilized to improve the visualization of the GPA-Tree model fit to collate or separate layers of the model (add or remove leaves).

Contructs a GPATree class object

Aastha Khatiwada

## Not run: 
library(GPATree)

# load GPATree example data
data(GPATreeExampleData)

# fitting the GPATree model
fit <- GPATree(GPATreeExampleData$gwasPval, GPATreeExampleData$annMat)

# get functional annotation information
leaf(fit)

# association mapping
assoc.gpatree <- assoc(fit, FDR = 0.01, fdrControl = 'global')

# pruning the GPATree model fit
pruned.fit <- prune(fit, cp = 0.005)

# plotting the GPATree model results
plot(fit)
plot(pruned.fit)

# run the ShinyGPATree app using output from the GPATree method
ShinyGPATree(fit)

## End(Not run)