R/TestSingleRegion.R

In rnaEditr: Statistical analysis of RNA editing sites and hyper-editing regions

#' Test associations between phenotype and RNA editing levels.
#'
#' @description Test associations between phenotype and
#'   RNA editing levels in a single site or summarized RNA editing levels
#'   in a single region.
#'
#' @param rnaEdit_num A named numeric vector of (summarized) RNA editing level
#'   values with sample IDs as names.
#' @param modelPrep_ls A list includes \code{modelFormula_char} which is 
#'   created by function \code{\link{MakeModelFormula}}, \code{pheno_df} which 
#'   is the input phenotype data frame in \code{\link{TestAssociations}}, and
#'   \code{minSize} (minimum sample size per group to use regular logistic 
#'   regression) which is created by function
#'   \code{\link{CountSamplesPerGroup}} when \code{respType} is \code{"binary"}.
#' @param respType Type of outcome. Defaults to \code{"binary"}. 
#' 
#' @return a dataframe with test statistics (\code{estimate, stdErr, pValue} or
#'   \code{coef, exp_coef, se_coef, pValue}).
#' 
#' @details \code{minSize} is used by function \code{TestSingleRegion} to 
#'   decide on whether to use regular logistic regression or Firth corrected 
#'   logistic regression (\url{"https://www.jstor.org/stable/2336755"}).  
#'
#' @importFrom stats as.formula binomial coef glm lm p.adjust drop1 vcov
#' @importFrom stats gaussian 
#' @importFrom survival Surv coxph
#' @importFrom logistf logistf 
#' 
#' @export
#' @keywords internal
#'
#' @examples
#'   data(rnaedit_df)
#'   
#'   exm_pheno <- readRDS(
#'     system.file(
#'     "extdata",
#'     "pheno_df.RDS",
#'     package = 'rnaEditr',
#'     mustWork = TRUE
#'     )
#'   )
#'   
#'   exm_model <- list(
#'     modelFormula_char = "age_at_diagnosis ~ rnaEditSummary",
#'     pheno_df = exm_pheno,
#'     minSize = NULL
#'   )
#'   
#'   TestSingleRegion(
#'     rnaEdit_num = unlist(rnaedit_df[2,]),
#'     modelPrep_ls = exm_model,
#'     respType = "continuous"
#'   )
#'
TestSingleRegion <- function(rnaEdit_num,
                             modelPrep_ls,
                             respType = c("binary", "continuous", "survival")){
  
    respType <- match.arg(respType)
    
    # Convert rnaEdit_num to rnaEditOne_df with sample names to be merged
    #   into final df later.
    rnaEditOne_df <- data.frame(
      sample = names(rnaEdit_num),
      rnaEditSummary = rnaEdit_num,
      row.names = NULL,
      stringsAsFactors = FALSE
    )
    
    # Merge pheno_df and rnaEditOne_df
    rnaEditOnePheno_df <- merge(
      x = modelPrep_ls$pheno_df,
      y = rnaEditOne_df,
      by = "sample"
    )
    
    # Fit model
    switch(
      respType,
      "binary" = {
        
        if (is.null(modelPrep_ls$minSize)) {
          stop(
  "minSize can't be NULL. Please use function CountSamplesPerGroup to find it!"
          )
        } else if (modelPrep_ls$minSize < 10) {
          
          .LOGISTFTest(
            modelFormula_char = modelPrep_ls$modelFormula_char,
            rnaEditOnePheno_df = rnaEditOnePheno_df
          )
          
        } else {
          
          .GLMTest(
            modelFormula_char = modelPrep_ls$modelFormula_char,
            rnaEditOnePheno_df = rnaEditOnePheno_df,
            family = binomial(link = "logit")
          )
          
        }
        
      },
      
      "continuous" = {
        
        .GLMTest(
          modelFormula_char = modelPrep_ls$modelFormula_char,
          rnaEditOnePheno_df = rnaEditOnePheno_df,
          family = gaussian(link = "identity")
        )
      
      },
      
      "survival" = {
        
        .CoxPHTest(
          modelFormula_char = modelPrep_ls$modelFormula_char,
          rnaEditOnePheno_df = rnaEditOnePheno_df
        )
        
      }
    )
  }
  
  
  # Firth Bias-Corrected Logistic
  .LOGISTFTest <- function(modelFormula_char, rnaEditOnePheno_df){
    
    f <- tryCatch({
      logistf(
        formula = as.formula(modelFormula_char),
        data = rnaEditOnePheno_df
      )
    }, warning = function(w){
      NULL
    }, error = function(e){
      NULL
    })
    
    if (is.null(f)) {
      
      result_df <- data.frame(
        estimate = NA_real_,
        stdErr = NA_real_,
        pValue = 1
      )
      
    } else {
      
      result_df <- data.frame(
        estimate = coef(f)["rnaEditSummary"],
        stdErr = sqrt(diag(vcov(f)))["rnaEditSummary"],
        pValue = drop1(f)["rnaEditSummary", "P-value"],
        stringsAsFactors = FALSE
      )
      rownames(result_df) <- NULL
      
    }
    
    result_df
    
  }
  
  
  # Ordinary Least Squares / Logistic
  .GLMTest <- function(modelFormula_char,
                       rnaEditOnePheno_df,
                       family){
    
    f <- tryCatch({
      glm(
        formula = as.formula(modelFormula_char),
        family = family,
        data = rnaEditOnePheno_df
      )
    }, warning = function(w){
      NULL
    }, error = function(e){
      NULL
    })
    
    if (is.null(f)) {
      
      result_df <- data.frame(
        estimate = NA_real_,
        stdErr = NA_real_,
        pValue = 1
      )
      
    } else {
      
      result_df <- data.frame(
        estimate = coef(summary(f))["rnaEditSummary", "Estimate"],
        stdErr = coef(summary(f))["rnaEditSummary", "Std. Error"],
        # Regardless of the statistical test, the p-value is in column four
        pValue = coef(summary(f))["rnaEditSummary", 4]
      )
      rownames(result_df) <- NULL
      
    }
    
    result_df
    
  }
  
  
  # CoxPH
  .CoxPHTest <- function(modelFormula_char, rnaEditOnePheno_df){
    
    f <- tryCatch({
      coxph(
        formula = as.formula(modelFormula_char),
        data = rnaEditOnePheno_df
      )
    }, warning = function(w){
      NULL
    }, error = function(e){
      NULL
    })
    
    if (is.null(f)) {
      
      result_df <- data.frame(
        coef = NA_real_,
        exp_coef = NA_real_,
        se_coef = NA_real_,
        pValue = 1
      )
      
    } else {
      
      result_df <- data.frame(
        coef = coef(summary(f))["rnaEditSummary", "coef"],
        exp_coef = coef(summary(f))["rnaEditSummary", "exp(coef)"],
        se_coef = coef(summary(f))["rnaEditSummary", "se(coef)"],
        pValue = coef(summary(f))["rnaEditSummary", "Pr(>|z|)"]
      )
      rownames(result_df) <- NULL
      
    }
    
    result_df
  
}