feateff: Extracts treatment, the outcome on which the effect is...

In teff-package/teff: Profiles and targets subpopulations with high Treatment EEFects

Extracts treatment, the outcome on which the effect is assessed, and feature data from transcriptomic and methylomic studies

Description

Extracts treatment, the outcome on which the effect is assessed, and feature data from transcriptomic and methylomic studies

Usage

feateff(
  set,
  tname,
  effname,
  reft = NULL,
  refeff = NULL,
  event = NULL,
  covnames = NULL,
  covtype = NULL,
  sva = FALSE,
  betas = TRUE,
  UsegeneSymbol = FALSE,
  rnaseq = FALSE
)

Arguments

set	GenomicRatioSet, eSet derived object or SummarizedExperiment
tname	character with name of treatment variable
effname	character with name of outcome variable on which the effect is measured (time to event in the case of survival)
reft	character vector that indicates no-treatment and treatment levels (Default: NULL)
refeff	character vector that indicates no-outcome and outcome levels when the outcome on which the effect is measured is categorical (Default: NULL)
event	character with name of event variable in the case of a survival outcome (Default: NULL)
covnames	character vector with names of covariates (Default: NULL)
covtype	character vector with character "n" indicates which covariates are numerical e.g. c("n", "n", "c") (Default: NULL)
sva	logical indicates whether surrogate variable should be added as covariates (Default: NULL)
betas	logical indicates whether beta values be used if set is a GenomicRatioSet (Default: TRUE)
UsegeneSymbol	logical indicates whether genenames should be used as feature names (Default: FALSE)
rnaseq	logical indicates if expression data is RNA-seq (TRUE) or microarray (FALSE, default)

Details

This function extracts feature and treatment-effects data, from eSet or SummarizedExperiment objects for profiling with predicteff. The function includes the option of adding surrogate variables as additional covariates to the treatment-effect data.

Examples

#library(GEOquery)
#gsm <- getGEO("GSE17755")
#gsm <- gsm[[1]]

#in this example we use sex as the treatment variable do detect groups of
# high sexual dimorphism in arthritis disease.

#data4teff <- feateff(gsm, tname="gender:ch1", reft=c("male", "female"),
#                      effname="disease:ch1", refeff=c("healthy","arthritis"),
#                      covnames="age:ch1", covtype="n",
#                      sva=TRUE, UsegeneSymbol=TRUE)

teff-package/teff documentation built on March 20, 2022, 8:25 p.m.