inst/md/omicIntegration.md

In BGLR: Bayesian Generalized Linear Regression

Integrating multiple Omics for Prediction of BC Survival

The following scripts illustrate how to fit some of the models presented in Vazquez et al., Genetics (2016).

(1) Installing required libraries

The code below illustrates how to install BGLR and BGData from GitHub. BGLR can also be installed from CRAN using install.packages().

 install.packages(pkg='devtools',repos='https://cran.r-project.org/')    #1# install devtools
 library(devtools)                                                       #2# load the library
 install_git('https://github.com/gdlc/BGLR/')                            #3# install BGLR from GitHub
 install_git('https://github.com/quantgen/BGData/')                      #4# install BGLR from GitHub

(2) Loading data and computing similarity matrices.

The function getG(), from the BGData R-package computes a similarity matrix of the form G=XX'. The function offers several alternatives relative to centering and scaling. The function also allows multi-core computing. For further details follow the link provided above.

Data. The code assumes that the user has saved in the file OMIC_DATA.RDasta the objects that contain the phenotypic, covariates and omic information * XF:

 library(BGData)
 library(BGLR)
 load('OMIC_DATA.RData')

 Gge<-getG(Xge,scaleCol=T,scaleG=T) # Similarity matrix for gene expression.
 Gmt<-getG(Xmt,scaleCol=T,scaleG=T) # Similarity matrix for methylation. 
 XF<- scale(XF, scale=FALSE, center=TRUE) # centering and scaling the incidence matrix for fixed effects.

(3) Fitting a survival model for Fixed effects using BGLR

The following code illustrates how to use BGLR to fit a fixed effects model. The matrix XF is an incidence matrix for effects. There is no column for intercept in XF because BGLR adds the intercept authomatically. The response variable y is assumed to be coded with two lables (e.g., 0/1), the argument response_type is used to indicate to BGLR that the response is ordinal (the binary case is a special case with only two levels). Predictors are given to BGLR in the form a two-level list. The argument save_at can be used to provide a path and a pre-fix to be added to the files saved by BGLR. For further details see Pérez-Rodriguez and de los Campos, Genetics, 2014 The code also shows how to retrieve estimates of effects and of psuccess probabilities.

 # Inputs
  ETA<-list( COV=list(X=XF, model='FIXED') )
  nIter=12000; burnIn=2000

 # Fitting the model
  fm=BGLR(y=y, ETA=LP,nIter=nIter,burnIn=burnIn, saveAt='cov_', response_type='ordinal')

 # Retrieving estimates
  fm$ETA$COV$b      # posterior means of fixed effects
  fm$ETA$COV$SD.b   # posteriro SD of fixed effects
  head(fm$probs)    # estimated probabilities for the 0/1 outcomes.

BGLR documentation built on May 12, 2022, 1:06 a.m.