Evaluation of methods for oligonucleotide array data via quantitative real-time PCR.
BACKGROUND
There are currently many different methods for processing and summarizing probe-level data from Affymetrix oligonucleotide arrays. It is of great interest to validate these methods and identify those that are most effective. There is no single best way to do this validation, and a variety of approaches is needed. Moreover, gene expression data are collected to answer a variety of scientific questions, and the same method may not be best for all questions. Only a handful of validation studies have been done so far, most of which rely on spike-in datasets and focus on the question of detecting differential expression. Here we seek methods that excel at estimating relative expression. We evaluate methods by identifying those that give the strongest linear association between expression measurements by array and the "gold-standard" assay. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) is generally considered the "gold-standard" assay for measuring gene expression by biologists and is often used to confirm findings from microarray data. Here we use qRT-PCR measurements to validate methods for the components of processing oligo array data: background adjustment, normalization, mismatch adjustment, and probeset summary. An advantage of our approach over spike-in studies is that methods are validated on a real dataset that was collected to address a scientific question.


RESULTS
We initially identify three of six popular methods that consistently produced the best agreement between oligo array and RT-PCR data for medium- and high-intensity genes. The three methods are generally known as MAS5, gcRMA, and the dChip mismatch mode. For medium- and high-intensity genes, we identified use of data from mismatch probes (as in MAS5 and dChip mismatch) and a sequence-based method of background adjustment (as in gcRMA) as the most important factors in methods' performances. However, we found poor reliability for methods using mismatch probes for low-intensity genes, which is in agreement with previous studies.


CONCLUSIONS
We advocate use of sequence-based background adjustment in lieu of mismatch adjustment to achieve the best results across the intensity spectrum. No method of normalization or probeset summary showed any consistent advantages.
Qin
Li-Xuan
LX
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA. qinl@mskcc.org
Beyer
Richard P
RP
Hudson
Francesca N
FN
Linford
Nancy J
NJ
Morris
Daryl E
DE
Kerr
Kathleen F
KF
D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D017931:DNA Primers; D015342:DNA Probes; D003627:Data Interpretation, Statistical; D005069:Evaluation Studies as Topic; D015870:Gene Expression; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D051379:Mice; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D009841:Oligonucleotides / Q000737:chemistry*; D016133:Polymerase Chain Reaction; D012313:RNA / Q000378:metabolism; D015203:Reproducibility of Results; D020133:Reverse Transcriptase Polymerase Chain Reaction / Q000379:methods*; D012680:Sensitivity and Specificity
D017931:DNA Primers; D015342:DNA Probes; D009841:Oligonucleotides; D012313:RNA