## These examples may each take a few minutes to compute
## Obtain Dataset
data(arabid)
## Analysis of Count Data using Internal and Stability Validation Measures
count1 <- optCluster(arabid, 2:4, clMethods = "all", countData = TRUE)
summary(count1)
## Analysis of Count Data using All Validation Measures
if(require("Biobase") && require("annotate") && require("GO.db") &&
require("org.At.tair.db")){
count2 <- optCluster(arabid, 2:4, clMethods = "all", countData = TRUE, validation = "all",
annotation = "org.At.tair.db")
summary(count2)
}
## Normalize Data with Respect to Library Size
obj <- t(t(arabid)/colSums(arabid))
## Analysis of Normalized Data using Internal and Stability Validation Measures
norm1 <- optCluster(obj, 2:4, clMethods = "all")
summary(norm1)
## Analysis of Normalized Data using All Validation Measures
if(require("Biobase") && require("annotate") && require("GO.db") &&
require("org.At.tair.db")){
norm2 <- optCluster(obj, 2:4, clMethods = "all", validation = "all",
annotation = "org.At.tair.db")
summary(norm2)
}
## Analysis with Only UPGMA using Internal and Stability Validation Measures
hier1 <- optCluster(obj, 2:10, clMethods = "hierarchical")
summary(hier1)
## Analysis with Only UPGMA using All Validation Measures
if(require("Biobase") && require("annotate") && require("GO.db") &&
require("org.At.tair.db")){
hier2 <- optCluster(obj, 2:10, clMethods = "hierarchical", validation = "all",
annotation = "org.At.tair.db")
summary(hier2)
}
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