NormqPCR (version 1.18.0)

selectHKs: Selection of reference/housekeeping genes

Description

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

Usage

selectHKs(qPCRBatch, ...)
"selectHKs"(qPCRBatch, group, method = "geNorm", minNrHKs = 2, log = TRUE, Symbols,  trace = TRUE, na.rm = TRUE)
"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.

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)

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.

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)