gdsCoxSurv: Fit Cox survival to all variants from a standard IMPUTE2...

In suchestoncampbelllab/survivR: gwasurvivr: an R package for genome wide survival analysis

Description

Performs survival analysis using Cox proportional hazard models on imputed genetic data from GDS files.

Usage

gdsCoxSurv(gdsfile, covariate.file, id.column, sample.ids = NULL,
  time.to.event, event, covariates, inter.term = NULL,
  print.covs = "only", out.file, chunk.size = 5000,
  maf.filter = 0.05, flip.dosage = TRUE, verbose = TRUE,
  clusterObj = NULL)

Arguments

gdsfile	path to .gds file. Location of the .gds file should also contain .snp.rdata and .scan.rdata files.
covariate.file	data.frame comprising phenotype information, all covariates to be added in the model must be numeric.
id.column	character giving the name of the ID column in covariate.file.
sample.ids	character vector of sample IDs to keep in survival analysis
time.to.event	character of column name in covariate.file that represents the time interval of interest in the analysis
event	character of column name in covariate.file that represents the event of interest to be included in the analysis
covariates	character vector with exact names of columns in covariate.file to include in analysis
inter.term	character string giving the column name of the covariate that will be added to the interaction term with SNP (e.g. term*SNP). See details.
print.covs	character string of either "only", "all" or "some", defining which covariate statistics should be printed to the output. See details.
out.file	character of output file name (do not include extension)
chunk.size	integer number of variants to process per thread
maf.filter	numeric to filter minor allele frequency (i.e. choosing 0.05 means filtering MAF>0.05). User can set this to NULL if no filtering is preffered. Default is 0.05.
flip.dosage	logical to flip which allele the dosage was calculated on, default flip.dosage=TRUE
verbose	logical for messages that describe which part of the analysis is currently being run
clusterObj	A cluster object that can be used with the parApply function. See details.

Details

Testing for SNP-covariate interactions: User can define the column name of the covariate that will be included in the interaction term. For example, for given covariates a and b, where c is defined as the inter.term the model will be: ~ a + b + c + SNP + c*SNP.

Printing results of other covariates: print.covs argument controls the number of covariates will be printed as output. The function is set to only by default and will only print the SNP or if an interaction term is given, the results of the interaction term (e.g. SNP*covariate). Whereas, all will print results (coef, se.coef, p.value etc) of all covariates included in the model. some is only applicable if an interaction term is given and will print the results for SNP, covariate tested for interaction and the interaction term. User should be mindful about using the all option, as it will likely slow down the analysis and will increase the output file size.

User defined parallelization: This function uses parApply from parallel package to fit models to SNPs in parallel. User is not required to set any options for the parallelization. However, advanced users who wish to optimize it, can provide a cluster object generated by makeCluster family of functions that suits their need and platform.

Value

Saves two text files directly to disk: .coxph extension containing CoxPH survival analysis results. .snps_removed extension containing SNPs that were removed due to low variance or user-defined thresholds.

Examples

gdsfile <- system.file(package="gwasurvivr",
                       "extdata",
                       "gds_example.gds")
covariate.file <- system.file(package="gwasurvivr", 
                              "extdata",
                              "simulated_pheno.txt")
covariate.file <- read.table(covariate.file,
                             sep=" ",
                             header=TRUE,
                             stringsAsFactors = FALSE)
covariate.file$SexFemale <- ifelse(covariate.file$sex=="female", 1L, 0L)
sample.ids <- covariate.file[covariate.file$group=="experimental",]$ID_2
gdsCoxSurv(gdsfile=gdsfile,
           covariate.file=covariate.file,
           id.column="ID_2",
           sample.ids=sample.ids,
           time.to.event="time",
           event="event",
           covariates=c("age", "SexFemale", "DrugTxYes"),
           inter.term=NULL,
           print.covs="only",
           out.file="impute_example",
           chunk.size=50,
           maf.filter=0.005,
           flip.dosage=TRUE,
           verbose=TRUE,
           clusterObj=NULL)  
 

suchestoncampbelllab/survivR documentation built on March 10, 2021, 1:21 p.m.