Package 'gwasurvivr'

Title: gwasurvivr: an R package for genome wide survival analysis
Description: gwasurvivr is a package to perform survival analysis using Cox proportional hazard models on imputed genetic data.
Authors: Abbas Rizvi, Ezgi Karaesmen, Martin Morgan, Lara Sucheston-Campbell
Maintainer: Abbas Rizvi <[email protected]>
License: Artistic-2.0
Version: 1.23.0
Built: 2024-07-01 05:19:28 UTC
Source: https://github.com/bioc/gwasurvivr

Help Index

Fit Cox survival to all variants from a standard IMPUTE2 output after genotype imputation

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)

Fit Cox survival to all variants from a standard IMPUTE2 output after genotype imputation

Description

Performs survival analysis using Cox proportional hazard models on imputed genetic data from IMPUTE2 output

Usage

impute2CoxSurv(impute.file, sample.file, chr, covariate.file, id.column,
  sample.ids = NULL, time.to.event, event, covariates,
  inter.term = NULL, print.covs = "only", out.file,
  chunk.size = 10000, maf.filter = 0.05, exclude.snps = NULL,
  flip.dosage = TRUE, verbose = TRUE, clusterObj = NULL,
  keepGDS = FALSE)

Arguments

impute.file

character of IMPUTE2 file
sample.file

character of sample file affiliated with IMPUTE2 file
chr

numeric denoting chromosome number
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.
exclude.snps

a character vector listing the rsIDs of SNPs that will be excluded from analyses
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.
keepGDS

logical to keep GDS files (compressed IMPUTE2 files) after the analysis. Defaults to FALSE.

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

impute.file <- system.file(package="gwasurvivr",
                           "extdata",
                           "impute_example.impute2.gz")
sample.file <- system.file(package="gwasurvivr",
                           "extdata", 
                           "impute_example.impute2_sample")
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
impute2CoxSurv(impute.file=impute.file,
              sample.file=sample.file,
              chr=14,
              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,
              exclude.snps=NULL,
              flip.dosage=TRUE,
              verbose=TRUE,
              clusterObj=NULL,
              keepGDS=FALSE)

Fit Cox survival to all variants in a .vcf.gz file from Michigan imputation server

Description

Performs survival analysis using Cox proportional hazard models on imputed genetic data stored in compressed VCF files

Usage

michiganCoxSurv(vcf.file, covariate.file, id.column, sample.ids = NULL,
  time.to.event, event, covariates, inter.term = NULL,
  print.covs = "only", out.file, maf.filter = 0.05, r2.filter = NULL,
  chunk.size = 5000, verbose = TRUE, clusterObj = NULL)

Arguments

vcf.file

character(1) path to VCF file.
covariate.file

matrix(1) comprising phenotype (time, event) and additional covariate data.
id.column

character(1) providing exact match to sample ID column from covariate.file
sample.ids

character vector with sample ids to include in analysis
time.to.event

character(1) string that matches time column name in pheno.file
event

character(1) string that matches event column name in pheno.file
covariates

character vector with matching column names in pheno.file of covariates of interest
inter.term

character(1) 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(1) string of either "only", "all" or "some", defining which covariate statistics should be printed to the output. See details.
out.file

character(1) string with output name
maf.filter

integer(1) filter out minor allele frequency below threshold (i.e. 0.005 will filter MAF > 0.005)
r2.filter

integer(1) of imputation quality score filter (i.e. 0.7 will filter r2 > 0.7)
chunk.size

integer(1) number of variants to process per thread
verbose

logical(1) 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

vcf.file <- system.file(package="gwasurvivr",
                        "extdata",
                     "michigan.chr14.dose.vcf.gz")
pheno.fl <- system.file(package="gwasurvivr",
                        "extdata",
                     "simulated_pheno.txt")
pheno.file <- read.table(pheno.fl, 
                         sep=" ",
                         header=TRUE,
                         stringsAsFactors = FALSE)
pheno.file$SexFemale <- ifelse(pheno.file$sex=="female", 1L, 0L)
sample.ids <- pheno.file[pheno.file$group=="experimental",]$ID_2
michiganCoxSurv(vcf.file=vcf.file,
              covariate.file=pheno.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="michigan_example",
              r2.filter=0.3,
              maf.filter=0.005,
              chunk.size=50,
              verbose=TRUE,
              clusterObj=NULL)

Fit Cox survival to all variants from PLINK binary files (.BED, .BIM, .FAM)

Description

Performs survival analysis using Cox proportional hazard models on directly typed data in PLINK format

Usage

plinkCoxSurv(bed.file, covariate.file, id.column, sample.ids = NULL,
  time.to.event, event, covariates, inter.term = NULL,
  print.covs = "only", out.file, chunk.size = 10000,
  maf.filter = 0.005, flip.dosage = TRUE, verbose = TRUE,
  clusterObj = NULL)

Arguments

bed.file

character of name of plink files without extension
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

bed.file <- system.file(package="gwasurvivr",
                       "extdata",
                       "plink_example.bed")
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
plinkCoxSurv(bed.file=bed.file,
             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)

Fit Cox survival to all variants in a .vcf.gz file from Sanger imputation server

Description

Performs survival analysis using Cox proportional hazard models on imputed genetic data stored in compressed VCF files.

Usage

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

Arguments

vcf.file

character(1) path to VCF file.
covariate.file

matrix(1) comprising phenotype (time, event) and additional covariate data.
id.column

character(1) providing exact match to sample ID column from covariate.file
sample.ids

character vector with sample ids to include in analysis
time.to.event

character(1) string that matches time column name in pheno.file
event

character(1) string that matches event column name in pheno.file
covariates

character vector with matching column names in pheno.file of covariates of interest
inter.term

character(1) 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(1) string of either "only", "all" or "some", defining which covariate statistics should be printed to the output. See details.
out.file

character(1) string with output name
maf.filter

integer(1) filter out minor allele frequency below threshold (i.e. 0.005 will filter MAF > 0.005)
info.filter

integer(1) of imputation quality score filter (i.e. 0.7 will filter info > 0.7)
chunk.size

integer(1) number of variants to process per thread
verbose

logical(1) 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

vcf.file <- system.file(package="gwasurvivr",
                       "extdata",
                       "sanger.pbwt_reference_impute.vcf.gz")
pheno.fl <- system.file(package="gwasurvivr",
                        "extdata",
                     "simulated_pheno.txt")
pheno.file <- read.table(pheno.fl,
                         sep=" ",
                         header=TRUE,
                         stringsAsFactors = FALSE)
pheno.file$SexFemale <- ifelse(pheno.file$sex=="female", 1L, 0L)
sample.ids <- pheno.file[pheno.file$group=="experimental",]$ID_2
sangerCoxSurv(vcf.file=vcf.file,
              covariate.file=pheno.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="sanger_example",
              info.filter=0.3,
              maf.filter=0.005,
              chunk.size=50,
              verbose=TRUE,
              clusterObj=NULL)