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KODAMA

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<h2>Single-cell data</h2>
<p>The data set from Tasic et al. encompasses 23,822 cells from adult mouse cortex, split by the authors into 133 clusters with strong hierarchical organisation. A standard preprocessing pipeline consisting of sequencing depth normalisation, feature selection, log-transformation, and reducing the dimensionality to 50 PCs was applied as described by Kobak & Berens in <a href="https://www.nature.com/articles/s41467-019-13056-x" target="_blank">The art of using t-SNE for single-cell transcriptomics</a>.</p>

Tutorial

Download the data from here and unpack. Direct links: VISp, ALM. To get the information about cluster colors and labels (sample_heatmap_plot_data.csv), open the interactive data browser, go to "Sample Heatmaps", click "Build Plot!" and then "Download data as CSV".

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ta=read.csv("tasic-sample_heatmap_plot_data.txt") rownames(ta)=ta[,1] VIS=read.csv("mouse_VISp_gene_expression_matrices_2018-06-14/mouse_VISp_2018-06-14_exon-matrix.csv") ALM=read.csv("mouse_ALM_gene_expression_matrices_2018-06-14/mouse_ALM_2018-06-14_exon-matrix.csv") Copy

<p>The intron and exon data are merged and the zeros columns are removed.</p>

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data=t(cbind(ALM,VIS)) colnames(data)=as.character(data[1,]) data=data[-1,] ii=intersect(rownames(data),rownames(ta)) data=data[ii,] data=data[,colSums(data)!=0] near.zero.counts=colMeans(data<32) Copy

<p>The data are normalized and the converted to log ratios.</p>

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temp=data temp[temp<=32]=NA temp=log2(temp) m=colMeans(temp,na.rm = TRUE) y=exp(-1.5*(m-6.56))+0.02 data=data[,which(near.zero.counts>y)] su=rowSums(data) data=((data/su)*10^6)*median(su) data=log2(data+1) Copy

<p>The first 50 principal components are calculated.</p>

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pca=prcomp(data)$x[,1:50] Copy

<p>The KODAMA algorithm is then applied to the 50 PCA and t-SNE is used to visualize the KODAMA dissimilarity matrix.</p>

<div class="code-container">
    <pre>

kk=KODAMA.matrix(pca) res_KODAMA_tSNE <- KODAMA.visualization(kk) plot(res_KODAMA_tSNE,pch=21,bg=ta[,"cluster_color"],main="KODAMA", xlab= "First dimension", ylab = "Second dimension") Copy

<p>The KODAMA clustering are then visualized</p>

<p align="center">
    <img src="https://gist.github.com/assets/168087487/ecb08dea-0c8d-4822-83fc-8cb406ff9a4c" alt="KODAMA Visualization" height="500" width="500" />
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