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

Abstract

1.AbstractPersonalized 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. Approaching this question, we used two different unsupervised dimension reduction methods – t-SNE and UMAP – on three different metastases datasets – prostate cancer, neuroendocrine prostate cancer, and skin cutaneous melanoma – including 682 different samples, with three different underlying data transformations – unprocessed FPKM values, log10 transformed FPKM values, and log10+1 transformed FPKM values – to visualize potential underlying clusters. The approaches resulted in formation of different clusters that were independent of respective resection sites. Additionally, data transformation critically affected cluster formation in most cases. Of note, our study revealed no tight link between the metastasis resection site and specific transcriptomic features. Instead, our analysis demonstrates the dependency of cluster formation on the underlying data transformation and the dimension reduction method applied. These observations propose data transformation as another key element in the interpretation of visual clustering approaches apart from well-known determinants such as initialization and parameters. Furthermore, the results show the need for further evaluation of underlying data alterations based on the biological question and subsequently used methods and applications.