self-organizing map (SOM) 是一种被无监督学习训练产生的一个低维（通常为二维)的人工神经网络，可作为一种进行降维的方法。在高通量测序数据中可作为一种将不同样本的特征聚类的可视化方式。
R 实现：oposSOM包
oposSOM软件包仅需要输入以基因表达矩阵数据，例如经过标准化处理microarray 数据或RNA-seq数据。
处理过程：
• The SOM space obtained from the training process is characterized by several supporting maps and profiles providing, e.g. the number of genes mapped to each meta-gene.
• Samples are individually portrayed in PDF report sheets allowing
the detailed examination of their expression landscapes and especially to identify modules of co-expressed genes.
• Feature maps, reports and lists allow feature selection and evaluation of their statistical significance.
• Gene set enrichment analysis of the expression modules provides
their functional context based on a large collection of predefined
gene sets.
• Sample diversity analysis and class discovery is performed using
multiple algorithms (e.g. hierarchical clustering, correlation
spanning tree) and different metrics (Euclidean distance,
Pearson’s correlation coefficient).
1.安装oposSOM 包
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("oposSOM")
library(oposSOM)
env <- opossom.new(list(dataset.name="Tissues",dim.1stLvlSom=20))
data(opossom.tissues)
env$indata <- opossom.tissues
env$group.labels <- c(rep("Homeostasis", 2),"Endocrine","Digestion","Exocrine","Epithelium","Reproduction","Muscle",rep("Immune System", 2),rep("Nervous System", 2) )
env$group.colors <- c(rep("gold", 2),"red2","brown","purple","cyan","pink","green2",rep("blue2", 2),rep("gray", 2) )
opossom.run(env)
具体介绍可见：The oposSOM Package
参考：
https://zhuanlan.zhihu.com/p/73534694
https://www.fmi.uni-leipzig.de/Media/DissAbstracts/abstract.wirth.pdf
