Colantuoni Carlo, Henry George, Zeger Scott, Pevsner Jonathan
Department of Neurology, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD 21205, USA.
Bioinformatics. 2002 Nov;18(11):1540-1. doi: 10.1093/bioinformatics/18.11.1540.
SNOMAD is a collection of algorithms for the normalization and standardization of gene expression datasets derived from diverse biological and technological sources. In addition to conventional transformations and visualization tools, SNOMAD includes two non-linear transformations which correct for bias and variance which are non-uniformly distributed across the range of microarray element signal intensities: (1). Local mean normalization; and (2). Local variance correction (Z-score generation using a locally calculated standard deviation).
SNOMAD是一组算法，用于对源自不同生物学和技术来源的基因表达数据集进行归一化和标准化处理。除了传统的转换和可视化工具外，SNOMAD还包括两种非线性转换，用于校正微阵列元件信号强度范围内非均匀分布的偏差和方差：（1）局部均值归一化；（2）局部方差校正（使用局部计算的标准差生成Z分数）。
