Effect of distance measures on confidences of t-SNE embeddings and its implications on clustering for scRNA-seq data.

ABSTRACT: Arguably one of the most famous dimensionality reduction algorithms of today is t-distributed stochastic neighbor embedding (t-SNE). Although being widely used for the visualization of scRNA-seq data, it is prone to errors as any algorithm and may lead to inaccurate interpretations of the visualized data. A reasonable way to avoid misinterpretations is to quantify the reliability of the visualizations. The focus of this work is first to find the best possible way to predict sample-based confidence scores for t-SNE embeddings and next, to use these confidence scores to improve the clustering algorithms. We adopt an RF regression algorithm using seven distance measures as features for having the sample-based confidence scores with a variety of different distance measures. The best configurat

SUBMITTER: Ozgode Yigin B 

PROVIDER: S-EPMC10121641 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature