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

Marquardt A, Kollmannsberger P, Krebs M, Argentiero A, Knott M, Solimando AG, Kerscher AG (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Genes Molecular Diversity Preservation International (MDPI)

Book Volume: 13

Article Number: 1335

Journal Issue: 8

DOI: 10.3390/genes13081335

Abstract

Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, 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 and 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 the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or 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 the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.

Involved external institutions

Universitätsklinikum Würzburg DE Germany (DE) Klinikum Stuttgart DE Germany (DE) Istituto Tumori Bari Giovanni Paolo II IT Italy (IT) Julius-Maximilians-Universität Würzburg DE Germany (DE)

How to cite

APA:

Marquardt, A., Kollmannsberger, P., Krebs, M., Argentiero, A., Knott, M., Solimando, A.G., & Kerscher, A.G. (2022). Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors—Dependencies and Novel Pitfalls. Genes, 13(8). https://doi.org/10.3390/genes13081335

MLA:

Marquardt, André, et al. "Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors—Dependencies and Novel Pitfalls." Genes 13.8 (2022).

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