gdsCoxSurv: Fit Cox survival to all variants from a standard IMPUTE2...
In gwasurvivr: gwasurvivr: an R package for genome wide survival analysis

Description Usage Arguments Details Value Examples

View source: R/gdsCoxSurv.R

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

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)

`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.

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.

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.

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)