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KODAMA

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                        Data Analyses
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                            href="https://moussakassim.github.io/Metabolomic-data/">Metabolomic
                            data</a>
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                            href="https://moussakassim.github.io/Single-cell-Data/">Single
                            cell RNA seq data</a>
                        <a class="dropdown-item"
                            href="https://moussakassim.github.io/Spatial-Transcriptomics/">Spatial
                            Transcriptomic data</a>
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Metabolomic Data

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.

Tutorial

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 -->

MDS, tSNE and UMAP

Different algorithms for dimensionality reduction are applied

res_MDS = cmdscale(dist(u))
res_tSNE = Rtsne(u)$Y
res_UMAP = umap(u)$layout

<!-- KODAMA Section -->

KODAMA

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 -->

Visualize the different clustering algorithms:

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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