Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays

BMC Bioinformatics

Chao Lu¹ 

[1]Microarray Facility, The Centre for Applied Genomics, The Hospital for Sick Children, 555 University Avenue, Elm Wing Room 10104, Toronto, Ontario M5G 1X8, Canada

关键词: scaling;    GeneChip;    oligonucleotide;    RNA;    DNA;    gene expression;    normalization;    Microarray;   

Others  :  1171664
DOI  :  10.1186/1471-2105-5-103

 received in 2003-07-17, accepted in 2004-07-29,  发布年份 2004

【 摘 要 】

Background

Normalization is an important step for microarray data analysis to minimize biological and technical variations. Choosing a suitable approach can be critical. The default method in GeneChip expression microarray uses a constant factor, the scaling factor (SF), for every gene on an array. The SF is obtained from a trimmed average signal of the array after excluding the 2% of the probe sets with the highest and the lowest values.

Results

Among the 76 U34A GeneChip experiments, the total signals on each array showed 25.8% variations in terms of the coefficient of variation, although all microarrays were hybridized with the same amount of biotin-labeled cRNA. The 2% of the probe sets with the highest signals that were normally excluded from SF calculation accounted for 34% to 54% of the total signals (40.7% ± 4.4%, mean ± sd). In comparison with normalization factors obtained from the median signal or from the mean of the log transformed signal, SF showed the greatest variation. The normalization factors obtained from log transformed signals showed least variation.

Conclusions

Eliminating 40% of the signal data during SF calculation failed to show any benefit. Normalization factors obtained with log transformed signals performed the best. Thus, it is suggested to use the mean of the logarithm transformed data for normalization, rather than the arithmetic mean of signals in GeneChip gene expression microarrays.

【 授权许可】

   
2004 Lu; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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