Hardin Johanna, Wilson Jason
Department of Mathematics, Pomona College, 610 North College Avenue, Claremont, CA 91711, USA.
Biostatistics. 2009 Jul;10(3):446-50. doi: 10.1093/biostatistics/kxp003. Epub 2009 Mar 10.
Novel techniques for analyzing microarray data are constantly being developed. Though many of the methods contribute to biological discoveries, inability to properly evaluate the novel techniques limits their ability to advance science. Because the underlying distribution of microarray data is unknown, novel methods are typically tested against the assumed normal distribution. However, microarray data are not, in fact, normally distributed, and assuming so can have misleading consequences. Using an Affymetrix technical replicate spike-in data set, we show that oligonucleotide expression values are not normally distributed for any of the standard methods for calculating expression values. The resulting data tend to have a large proportion of skew and heavy tailed genes. Additionally, we show that standard methods can give unexpected and misleading results when the data are not well approximated by the normal distribution. Robust methods are therefore recommended when analyzing microarray data. Additionally, new techniques should be evaluated with skewed and/or heavy-tailed data distributions.
用于分析微阵列数据的新技术不断涌现。尽管许多方法有助于生物学发现，但无法正确评估这些新技术限制了它们推动科学发展的能力。由于微阵列数据的潜在分布未知，新方法通常根据假定的正态分布进行测试。然而，事实上微阵列数据并非正态分布，如此假设可能会产生误导性后果。使用Affymetrix技术重复掺入数据集，我们表明，对于任何计算表达值的标准方法，寡核苷酸表达值都不是正态分布的。所得数据往往有很大比例的偏态和重尾基因。此外，我们表明，当数据不能很好地用正态分布近似时，标准方法可能会给出意想不到的和误导性的结果。因此，在分析微阵列数据时推荐使用稳健方法。此外，新技术应该用偏态和/或重尾数据分布进行评估。
