Article

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

2021-12-05

Abstract excerpt

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 (UM...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 727910d4-a7c1-55ab-b770-5b8c3e35db76
DOI: 10.1101/2021.12.03.471112

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors – Dependencies and Novel PitfallsDOI 10.1101/2021.12.03.471112Read

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors: Dependencies and Novel PitfallsSemantic proximity Read

Data-driven characterization of molecular phenotypes across heterogeneous sample collectionsSemantic proximity Read

Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic dataSemantic proximity Read

Diagnostic and Prognostic Implications of Ribosomal Protein Transcript Coordination in Human CancersSemantic proximity Read

Data-driven identification of prognostic tumor subpopulations using spatially mapped t-SNE of mass spectrometry imaging data.Semantic proximity Read

Mugen-UMAP: UMAP visualization and clustering of mutated genes in single-cell DNA sequencing dataSemantic proximity Read

Visualizing and interpreting single-cell gene expression datasets with Similarity Weighted Nonnegative EmbeddingSemantic proximity Read

Application of Cluster Analysis to Distant Metastases from Lung Cancer.Semantic proximity Read

Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic dataSemantic proximity Read

Miniature: Unsupervised glimpses into multiplexed tissue imaging datasets as thumbnails for data portalsSemantic proximity Read

oncotree2vec – A method for embedding and clustering of tumor phylogenetic treesSemantic proximity Read

Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data.Semantic proximity Read