TY  - JOUR
A1  - Marquardt, André
A1  - Kollmannsberger, Philip
A1  - Krebs, Markus
A1  - Argentiero, Antonella
A1  - Knott, Markus
A1  - Solimando, Antonio Giovanni
A1  - Kerscher, Alexander Georg
T1  - Visual clustering of transcriptomic data from primary and metastatic tumors — dependencies and novel pitfalls
JF  - Genes
N2  - 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.
KW  - visual clustering
KW  - t-SNE
KW  - UMAP
KW  - transcriptomic analysis
KW  - cancer
KW  - metastasis
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281872
SN  - 2073-4425
VL  - 13
IS  - 8
ER  -