A note on oligonucleotide expression values not being normally distributed
A note on oligonucleotide expression values not being normally distributed
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DOI：
10.1093/biostatistics/kxp003
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发表时间：
2009-07-01
期刊：
影响因子：
2.1
通讯作者：
Wilson, Jason
中科院分区：
文献类型：
--
作者：
Hardin, Johanna;Wilson, Jason
用于分析微阵列数据的新技术正在不断开发。尽管许多方法都有助于生物学发现，但无法正确评估新技术限制了它们推进科学的能力。由于微阵列数据的基本分布是未知的，因此通常针对假设的正态分布来测试新方法。然而，事实上，微阵列数据并不是正态分布的，假设如此可能会产生误导性的后果。使用 Affymetrix 技术重复掺入数据集，我们表明寡核苷酸表达值对于任何计算表达值的标准方法都不是正态分布的。所得数据往往具有很大比例的偏斜和重尾基因。此外，我们还表明，当数据不能很好地近似正态分布时，标准方法可能会给出意外且误导性的结果。因此，在分析微阵列数据时建议使用稳健的方法。此外，应使用倾斜和/或重尾数据分布来评估新技术。
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.