stabMeasureRho: Gene expression stability value rho
In jimrperkins/NormqPCR: Functions for normalisation of RT-qPCR data

Description Usage Arguments Details Value Author(s) References See Also Examples

Computation of the gene expression stability value rho for real-time quantitativ RT-PCR data. For more details we refer to Andersen et al. (2004).

  stabMeasureRho(x,...)

  ## S4 method for signature 'x'
stabMeasureRho(x, group, log = TRUE, na.rm = TRUE, returnAll = FALSE)

`x`	matrix containing real-time quantitative RT-PCR data, or qPCRBatch object
`...`	Extra arguments, detailed below
`group`	grouping factor, either a factor vector or a phenoData column called "Group"
`log`	logical: is data on log-scale
`na.rm`	a logical value indicating whether `NA` values should be stripped before the computation proceeds.
`returnAll`	logical, return additional information.

The gene expression stability value rho is computed. For more details see Andersen et al. (2004).

numeric vector with gene expression stability values

If returnAll == TRUE a list with the following components is returned

`rho`	stability measure rho of Andersen et al. (2004)
`d`	used by `selectHKs`
`v`	used by `selectHKs`

Matthias Kohl Matthias.Kohl@stamats.de

Claus Lindbjerg Andersen, Jens Ledet Jensen and Torben Falck Orntoft (2004). Normalization of Real-Time Quantitative Reverse Transcription-PCR Data: A Model-Based Variance Estimation Approach to Identify Genes Suited for Normalization, Applied to Bladder and Colon Cancer Data Sets. CANCER RESEARCH 64, 5245-5250, August 1, 2004. http://cancerres.aacrjournals.org/cgi/content/full/64/15/5245

Perkins, JR, Dawes, JM, McMahon, SB, Bennett, DL, Orengo, C, Kohl, M (2012). ReadqPCR and NormqPCR: R packages for the reading, quality checking and normalisation of RT-qPCR quantification cycle (Cq) data. BMC Genomics, 13, 1:296.

selectHKs

  data(Colon)
  Class <- pData(Colon)[,"Classification"]
  res.Colon <- stabMeasureRho(Colon, group = Class, log = FALSE)
  sort(res.Colon) # cf. Table 3 in Andersen et al (2004)
  
  data(Bladder)
  Grade <- pData(Bladder)[,"Grade"]
  res.Bladder <- stabMeasureRho(Bladder, group = Grade, log = FALSE)
  sort(res.Bladder) # cf. Table 3 in Andersen et al (2004)