Normalization methods for analysis of microarray gene-expression data.
This paper investigates subset normalization to adjust for location biases (e.g., splotches) combined with global normalization for intensity biases (e.g., saturation). A data set from a toxicogenomic experiment using the same control and the same treated sample hybridized to six different microarrays is used to contrast the different normalization methods. Simple t-tests were used to compare two samples for dye effects and for treatment effects. The numbers of genes that reproducibly showed significant p-values for the unnormalized data and normalized data from different methods were evaluated for assessment of different normalization methods. The one-sample t-statistic of the ratio of red to green samples was used to test for dye effects using only control data. For treatment effects, in addition to the one-sample t-test of the ratio of the treated to control samples, the two-sample t-test for testing the difference between treated and control samples was also used to compare the two approaches. The method that combines a subset approach (median or lowess fit) for location adjustment with a global lowess fit for intensity adjustment appears to perform well.
Chen
Yi-Ju
YJ
Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, USA.
Kodell
Ralph
R
Sistare
Frank
F
Thompson
Karol L
KL
Morris
Suzanne
S
Chen
James J
JJ
D015982:Bias; D003627:Data Interpretation, Statistical; D005456:Fluorescent Dyes; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data*; D015233:Models, Statistical; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods / Q000706:statistics & numerical data*
D005456:Fluorescent Dyes