Selection of optimal reference genes for normalization in quantitative RT-PCR.
BACKGROUND
Normalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introduce additional variability into normalized expression levels due to innate variation (between tissues, individuals, etc). To minimize this innate variability, multiple reference genes are used. Current methods of selecting reference genes make an assumption of independence in their innate variation. This assumption is not always justified, which may lead to selecting a suboptimal set of reference genes.


RESULTS
We propose a robust approach for selecting optimal subset(s) of reference genes with the smallest variance of the corresponding normalizing factors. The normalizing factor variance estimates are based on the estimated unstructured covariance matrix of all available candidate reference genes, adjusting for all possible correlations. Robustness is achieved through bootstrapping all candidate reference gene data and obtaining the bootstrap upper confidence limits for the variances of the log-transformed normalizing factors. The selection of the reference gene subset is optimized with respect to one of the following criteria: (A) to minimize the variability of the normalizing factor; (B) to minimize the number of reference genes with acceptable upper limit on variability of the normalizing factor, (C) to minimize the average rank of the variance of the normalizing factor. The proposed approach evaluates all gene subsets of various sizes rather than ranking individual reference genes by their stability, as in the previous work. In two publicly available data sets and one new data set, our approach identified subset(s) of reference genes with smaller empirical variance of the normalizing factor than in subsets identified using previously published methods. A small simulation study indicated an advantage of the proposed approach in terms of sensitivity to identify the true optimal reference subset in the presence of even modest, especially negative correlation among the candidate reference genes.


CONCLUSIONS
The proposed approach performs comprehensive and robust evaluation of the variability of normalizing factors based on all possible subsets of candidate reference genes. The results of this evaluation provide flexibility to choose from important criteria for selecting the optimal subset(s) of reference genes, unless one subset meets all the criteria. This approach identifies gene subset(s) with smaller variability of normalizing factors than current standard approaches, particularly if there is some nontrivial innate correlation among the candidate genes.
Chervoneva
Inna
I
Department of Pharmacology and Experimental Therapeutics, Thomas Jefferson University, Philadelphia, PA 19107, USA. I_Chervoneva@mail.jci.tju.edu
Li
Yanyan
Y
Schulz
Stephanie
S
Croker
Sean
S
Wilson
Chantell
C
Waldman
Scott A
SA
Hyslop
Terry
T
D020869:Gene Expression Profiling / Q000379:methods; D005796:Genes; D023281:Genomics / Q000379:methods*; D006162:Guanylate Cyclase / Q000235:genetics; D012333:RNA, Messenger; D000074261:Receptors, Enterotoxin; D050705:Receptors, Guanylate Cyclase-Coupled; D018000:Receptors, Peptide / Q000235:genetics; D020133:Reverse Transcriptase Polymerase Chain Reaction / Q000379:methods*
D012333:RNA, Messenger; D018000:Receptors, Peptide; D006162:Guanylate Cyclase; D000074261:Receptors, Enterotoxin; D050705:Receptors, Guanylate Cyclase-Coupled
Clin Cancer Res. 2006 Aug 1;12(15):4545-52
16899600
Biotechniques. 2004 Jan;36(1):84-6, 88, 90-1
14740490
Obes Res. 2005 Apr;13(4):649-52
15897472
Biotechniques. 2004 Jul;37(1):112-4, 116, 118-9
15283208
Biotechniques. 2005 Jul;39(1):75-85
16060372
Nucleic Acids Res. 2001 May 1;29(9):e45
11328886
Biotechniques. 2005 May;38(5):785-92
15945375
Biostatistics. 2006 Jan;7(1):130-44
16049138
Cancer Res. 2004 Aug 1;64(15):5245-50
15289330
Genome Biol. 2002 Jun 18;3(7):RESEARCH0034
12184808
Genome Biol. 2004;5(8):R59
15287981
Proc Natl Acad Sci U S A. 1996 Dec 10;93(25):14827-32
8962140
Genes Immun. 2005 Jun;6(4):279-84
15815687
J Biochem Biophys Methods. 2000 Nov 20;46(1-2):69-81
11086195