Preferred analysis methods for Affymetrix GeneChips revealed by a wholly defined control dataset.
BACKGROUND
As more methods are developed to analyze RNA-profiling data, assessing their performance using control datasets becomes increasingly important.


RESULTS
We present a 'spike-in' experiment for Affymetrix GeneChips that provides a defined dataset of 3,860 RNA species, which we use to evaluate analysis options for identifying differentially expressed genes. The experimental design incorporates two novel features. First, to obtain accurate estimates of false-positive and false-negative rates, 100-200 RNAs are spiked in at each fold-change level of interest, ranging from 1.2 to 4-fold. Second, instead of using an uncharacterized background RNA sample, a set of 2,551 RNA species is used as the constant (1x) set, allowing us to know whether any given probe set is truly present or absent. Application of a large number of analysis methods to this dataset reveals clear variation in their ability to identify differentially expressed genes. False-negative and false-positive rates are minimized when the following options are chosen: subtracting nonspecific signal from the PM probe intensities; performing an intensity-dependent normalization at the probe set level; and incorporating a signal intensity-dependent standard deviation in the test statistic.


CONCLUSIONS
A best-route combination of analysis methods is presented that allows detection of approximately 70% of true positives before reaching a 10% false-discovery rate. We highlight areas in need of improvement, including better estimate of false-discovery rates and decreased false-negative rates.
Choe
Sung E
SE
Department of Genetics, Harvard Medical School, New Research Building, 77 Avenue Louis Pasteur, Boston, MA 02115, USA. sung_choe@post.harvard.edu
Boutros
Michael
M
Michelson
Alan M
AM
Church
George M
GM
Halfon
Marc S
MS
D000465:Algorithms; D000818:Animals; D004330:Drosophila / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015345:Oligonucleotide Probes; D012333:RNA, Messenger / Q000032:analysis
D015345:Oligonucleotide Probes; D012333:RNA, Messenger