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The data belong to a cohort of 22 healthy donors (11 male and 11 female) where each provided about 40 urine samples over the time course of approximately 2 months, for a total of 873 samples. Each sample was analysed by Nuclear Magnetic Resonance Spectroscopy. Each spectrum was divided in 450 spectral bins.
Here, we load the MetRef dataset. Columns with only zero values are removed.
data(MetRef)
u = MetRef$data
u = u[,-which(colSums(u) == 0)]
We apply the Probabilistic Quotient Normalization
u = normalization(u)$newXtrain
We mean-center and univariate scaling the data set.
u = scaling(u)$newXtrain
Two classification vectors are created
class = as.numeric(as.factor(MetRef$gender))
class2 = as.numeric(as.factor(MetRef$donor))
<!-- MDS, tSNE and UMAP Section -->
<!-- KODAMA Section -->
We apply KODAMA with Partial Least Square Discriminant Analysis (PLS-DA) as classifier with 50 components to drive the accuracy maximization. The KODAMA dissimilarity matrix is converted in a low dimensionality space using three different methods (i.e., MDS, t-SNE, and UMAP).
kk = KODAMA.matrix(u, f.par = 50)
res_KODAMA_MDS = KODAMA.visualization(kk, method = "MDS")
res_KODAMA_tSNE = KODAMA.visualization(kk, method = "t-SNE")
res_KODAMA_UMAP = KODAMA.visualization(kk, method = "UMAP")
<!-- Visualize the different clustering algorithms Section -->
a) Labelled by the gender
par(mfrow = c(2, 3))
plot(res_MDS, pch = 21, bg = rainbow(2)[class], main = "MDS")
plot(res_tSNE, pch = 21, bg = rainbow(2)[class], main = "tSNE")
plot(res_UMAP, pch = 21, bg = rainbow(2)[class], main = "UMAP")
plot(res_KODAMA_MDS, pch = 21, bg = rainbow(2)[class], main = "KODAMA_MDS")
plot(res_KODAMA_tSNE, pch = 21, bg = rainbow(2)[class], main = "KODAMA_tSNE")
plot(res_KODAMA_UMAP, pch = 21, bg = rainbow(2)[class], main = "KODAMA_UMAP")
<p>b) Labelled by the donor</p>
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<pre><code>plot(res_MDS, pch = 21, bg = rainbow(22)[class2], main = "MDS")
plot(res_tSNE, pch = 21, bg = rainbow(22)[class2], main = "tSNE") plot(res_UMAP, pch = 21, bg = rainbow(22)[class2], main = "UMAP") plot(res_KODAMA_MDS, pch = 21, bg = rainbow(22)[class2], main = "KODAMA_MDS") plot(res_KODAMA_tSNE, pch = 21, bg = rainbow(22)[class2], main = "KODAMA_tSNE") plot(res_KODAMA_UMAP, pch = 21, bg = rainbow(22)[class2], main = "KODAMA_UMAP")
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