DGEobj.utils (version 1.0.6)

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

Description

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.

Usage

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

Arguments

contrastList

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

pvalField

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)

Value

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

Details

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 10.1007/978-3-642-04898-2_248 for a brief introduction to FDRs and q-values.

Examples

# 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,
                          -Qvalue,
                          -qvalue.lfdr)
   colnames(contrastList[[1]])

   contrastList <- runQvalue(contrastList)

   # note new columns added
   colnames(contrastList[[1]])
# }
# NOT RUN {
# }