Unsupervised learning (no date) [incomplete]
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Here are some documented examples of how to use unsupervised learning methods of the analysis of microarray data.
The examples in this section will use the Khan data set that is part of the DNAMR library. To read in the Khan data set, use the following commands:
rDirectory <- "c:/Program Files/R/rw1090"
FileName <- "/library/DNAMR/data/Khan.txt"
kh.dat <- read.table(file=paste(rDirectory,FileName,sep=""))
kh.log <- log(kh.dat,base=2)[,-1]
[,-1]at the end of the last command omits the first column from the data set before computing logs, since the first column is an image id.
Unsupervised learning (class discovery)
Hierarchical clustering, partitioning clustering (silhouette widths), model based clustering.
Clustering methods in the Acuity software system:
- Hierarchical Clustering
- K-Means Clustering
- K-Medians Clustering
- Gap Statistic
- Self-Organizing Maps
- Gene Shaving
- Principal Components Analysis