Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors: Dependencies and Novel Pitfalls

Abstract

Personalized Oncology is a rapidly evolving area and offers cancer patients therapy options more specific than ever. Yet, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation Projection (UMAP)) on three datasets of metastases (n=682 samples) with three different data transformations (unprocessed, log10 as well as log10+1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n=616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between site of resection and cluster formation outcome could be demonstrated, neither for datasets consisting of solely metastasis nor mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results indicate only minor transcriptional differences for metastases and corresponding primary tumors.