runQvalue: Calculate and add q-value and lFDR to dataframe

View source: R/runQvalue.R

runQvalueR Documentation

Calculate and add q-value and lFDR to dataframe


Takes an list of contrasts (e.g. topTable output or other dataframes that contain a p-value column). Adds a q-value and local FDR (lFDR) column to each dataframe.


runQvalue(contrastList, pvalField = "P.Value", ...)



A list of dataframes with a p-value column (all tables must use the same colname for the p-value column.)


Define the colname of the p-value field in each dataframe. Not needed if using topTable output. (Optional. Default = "P.Value")


Optional arguments passed to the qvalue function (See ?qvalue)


The qvalue package from John Storey at Princeton takes a list of p-values and calculates a q-value and a Local FDR (lFDR). The q-value is essentially a less conservative FDR estimate compared to the default Benjamini-Hochberg FDR produced by topTable analysis (i.e. will give more differential genes at the same nominal cutoff). The q-value function also produces a Local FDR (lFDR) column which answers a slightly different and possibly more relevant question. The BH FDR (adj.P.Val in topTable data.frames) and q-value gives the false discovery rate is for a list of genes at a given threshold. The local FDR attempts to answer the question: what is the probability that this particular gene is a false discovery? See doi: 10.1007/978-3-642-04898-2_248 for a brief introduction to FDRs and q-values.


The input contrastList now containing q-value and lFDR columns in each dataframe.


## Not run: 
   # NOTE: Requires the qvalue package

   dgeObj <- readRDS(system.file("exampleObj.RDS", package = "DGEobj"))
   contrastList <- DGEobj::getType(dgeObj, type = "topTable")
   contrastList <- lapply(contrastList, dplyr::select,

   contrastList <- runQvalue(contrastList)

   # note new columns added

## End(Not run)

DGEobj.utils documentation built on May 20, 2022, 1:08 a.m.