@@ -154,6 +154,8 @@ computeFdr <- function(df_out, df_null){

 #' fitAndEvalDataset

 #' @param df_null data frame containing results from analysis by

 #' bootstrapNull

+#' @param squeezeDenominator logical indicating whether F statistic

+#' denominator should be shrinked using limma::squeezeVar

 #' 

 #' @return data frame annotating each protein with a FDR based on 

 #' it's F statistic and number of observations

@@ -176,12 +178,18 @@ computeFdr <- function(df_out, df_null){

 #' @import dplyr

 #' @importFrom stats density

 #' @importFrom stats p.adjust

-getPvalues <- function(df_out, df_null){

+getPvalues <- function(df_out, df_null,

+                       squeezeDenominator = FALSE){

     dataset <- nObs <- nObsRound <- F_statistic <- 

         is_decoy <- max_rank <- true_cumsum <- 

         null_cumsum <- representative <- clustername <- 

         dataset <- FDR <- all_true <- all_null <- NULL

+    if(squeezeDenominator){

+        df_out <- .shrinkFstat(df_out, trueOrNull = "true")

+        df_null <- .shrinkFstat(df_null, trueOrNull = "null")

+    }

+    

     out_df <- bind_rows(df_out %>% mutate(dataset = "true"),

                         df_null) %>%

         mutate(nObsRound = round(nObs/10)*10) %>%

@@ -5,7 +5,7 @@

 \title{Compute p-values for given F statistics based on true and

 null dataset}

 \usage{

-getPvalues(df_out, df_null)

+getPvalues(df_out, df_null, squeezeDenominator = FALSE)

 }

 \arguments{

 \item{df_out}{data frame containing results from analysis by

@@ -13,6 +13,9 @@ fitAndEvalDataset}

 \item{df_null}{data frame containing results from analysis by

 bootstrapNull}

+

+\item{squeezeDenominator}{logical indicating whether F statistic

+denominator should be shrinked using limma::squeezeVar}

 }

 \value{

 data frame annotating each protein with a FDR based on