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Fig. 1 | BMC Cancer

Fig. 1

From: Diagnostic and prognostic implications of ribosomal protein transcript expression patterns in human cancers

Fig. 1

t-SNE better identifies clusters of RPT expression than PCA. a. Relative expression of RPTs in normal tissues from five cohorts was analyzed with PCA. In both methods, clustering occurs when samples possess similar underlying patterns of variation. t-SNE provides more distinct clusters that better associate with tissue of origin, indicating that normal tissues have distinct patterns of RPT expression. Axes are not labeled with t-SNE, as points are not mapped linearly and axes are not directly interpretable. b. Similar analyses in tumors. PCA clusters are poorly defined and do not correlate strongly with tumor type. t-SNE clusters are distinct and strongly associate with cancer type, indicating that tumors possess unique patterns of RPT expression based on their tissue of origin. c. Combined t-SNE analysis of RPT expression in normal tissue and tumor samples. Normal tissues and tumors cluster together but can be distinguished from one another, indicating that the latter retain a pattern of RPT expression resembling that of the normal tissue from which they originated. d. Many single cancer cohorts demonstrate sub-clustering by t-SNE. Clustering of six cohorts are provided as examples here. The number of clusters found in each cohort is listed in Additional file 1: Table S1. e. 3D area map of RPT relative expression in tumors from two cancer cohorts, sorted by cluster. The x-axis represents individual tumors, the z-axis represents individual RPTs, and the y-axis represents deviation from the mean relative expression. Cluster 2 of prostate cancer and Cluster 3 of HCC are both comprised of tumors with high relative expression of RPL8 and low RPL3. See Additional file 1: Figure S1, S2, and S5 for additional t-SNE plots of tumors and normal tissues. Perplexity settings for t-SNE analyses are designated in each plot by “P:”. For all analyses, learning rate (epsilon) = 10 and iterations = 5000

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