selectHKs: Selection of reference/housekeeping genes

In jimrperkins/NormqPCR: Functions for normalisation of RT-qPCR data

Description

This function can be used to determine a set of reference/housekeeping (HK) genes for gene expression experiments

Usage

selectHKs(qPCRBatch, ...)

## S4 method for signature 'matrix'
selectHKs(qPCRBatch, group, method = "geNorm", minNrHKs = 2, log = TRUE, Symbols, 
        trace = TRUE, na.rm = TRUE)

## S4 method for signature 'qPCRBatch'
selectHKs(qPCRBatch, group, method = "geNorm", minNrHKs = 2, log = TRUE, Symbols, 
	      trace = TRUE, na.rm = TRUE)

Arguments

qPCRBatch	matrix or qPCRBatch, containing the data (expression matrix) in the exprs slot
...	Extra arguments, detailed below
group	optional factor not used by all methods, hence may be missing
method	method to compute most stable genes
minNrHKs	minimum number of HK genes that should be considered
log	logical: is data on log-scale
Symbols	gene symbols
trace	logical, print additional information
na.rm	a logical value indicating whether NA values should be stripped before the computation proceeds.

Details

This function can be used to determine a set of reference/housekeeping (HK) genes for gene expression experiments. The default method "geNorm" was proposed by Vandesompele et al. (2002).

Currently, the geNorm method by Vandesompele et al. (2002) and the NormFinder method of Andersen et al. (2004) are implemented.

Vandesompele et al. (2002) propose a cut-off value of 0.15 for the pairwise variation. Below this value the inclusion of an additional housekeeping gene is not required.

Value

If method = "geNorm" a list with the following components is returned

ranking	ranking of genes from best to worst where the two most stable genes cannot be ranked
variation	pairwise variation during stepwise selection
meanM	average expression stability M

If method = "NormFinder" a list with the following components is returned

ranking	ranking of genes from best to worst where the two most stable genes cannot be ranked
rho	stability measure rho of Andersen et al. (2004)

Author(s)

Matthias Kohl [e-mail]

References

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.

Jo Vandesompele, Katleen De Preter, Filip Pattyn et al. (2002). Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology 2002. 3(7):research0034.1-0034.11. http://genomebiology.com/2002/3/7/research/0034/

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

Examples

  data(geNorm)
  tissue <- as.factor(c(rep("BM", 9), rep("FIB", 20), rep("LEU", 13),
                        rep("NB", 34), rep("POOL", 9)))
  res.BM <- selectHKs(geNorm.qPCRBatch[,tissue == "BM"], method = "geNorm", 
                      Symbols = featureNames(geNorm.qPCRBatch), minNrHK = 2, 
                      log = FALSE)

jimrperkins/NormqPCR documentation built on March 6, 2020, 1:06 a.m.