Map > Problem Definition > Data Preparation > Data Exploration > Multivariate
 

Data Exploration - Multivariate Analysis
I. Data Preparation

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

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. PCA (Principal Components Analysis)
d1 <- data
d1$geo_accession <- NULL
d1$gage <- NULL
d1$gender <- NULL
d1$target <- NULL

pc <- prcomp(d1)

groups <- as.fumeric(data$target)
plot(pc$x[, 1], pc$x[, 2], main = "PCA", col=groups, xlab = "PC1", ylab = "PC2")

 
This PCA is equivalent to performing the SVD on the centered data, where the centering occurs on the columns (here probes).
cx <- sweep(d1, 2, colMeans(d1), "-")
sv <- svd(cx)
names(sv)

plot(sv$d^2/sum(sv$d^2), xlim = c(0, 15), type = "b", pch = 16, xlab = "principal components", ylab = "variance explained", main="GSE74763_rawlog")

 
III. 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])

 
IV. Bioada SmartArray
Watch this video to learn how you can perform multivariate analysis using Bioada SmartArray significantly faster and easier.