Map > Problem Definition > Data Preparation > Data Exploration > Modeling > Clustering
 

Modeling - Clustering
I. Data Preparation

1- Load libraries
library(data.table)
library(formattable)
library(plotrix)
library(limma)
library(dplyr)
library(Rtsne)
library(MASS)
library(xgboost)
library(factoextra)
 

2- Read Expressions file
df <- read.csv("GSE74763_rawlog_expr.csv")
df2 <- df[,-1]
rownames(df2) <- df[,1]
expr <- transpose(df2)
rownames(expr) <- colnames(df2)
colnames(expr) <- rownames(df2)
dim(expr)
 

3- Read Samples file
targets <- read.csv("GSE74763_rawlog_targets.csv")
colnames(targets)
dim(targets)
 

4- Merge Expressions with Samples
data <- cbind(expr, targets)
colnames(data)
dim(data)
 
II. Heatmap
heatmap(as.matrix(expr[1:20, 1:20]))

 
II. Hierarchical Clustering
# Dissimilarity matrix
d <- dist(expr[1:20, 1:400], method = "euclidean")

# Hierarchical clustering using Complete Linkage
hc1 <- hclust(d, method = "complete" )

# Plot the obtained dendrogram
plot(hc1, cex = 1, hang = 0.1)

 
III. K-Means Clustering

k2 <- kmeans(expr, centers = 2, nstart = 25)
str(k2)
fviz_cluster(k2, data = expr, main = "K-Means", labelsize = 8)

 
IV. t-SNE (t-distributed Stochastic Neighbour Embedding)
d1 <- data
d1$geo_accession <- NULL
d1$gage <- NULL
d1$gender <- NULL
d1$target <- NULL

groups <- as.fumeric(data$target)

colors = rainbow(length(unique(groups)))
names(colors) = unique(groups)
tsne <- Rtsne(d1, dims = 2, perplexity=30, verbose=TRUE, max_iter = 500)
plot(tsne$Y, col=colors[groups], main="GSE74763_rawlog")
text(tsne$Y, labels=groups, col=colors[groups])

 
V. Bioada SmartArray
Watch this video to learn how you can build clustering models using Bioada SmartArray significantly faster and easier. Bioada only supports t-SNE.