Biology subjects

Bombina, P.

Publications and source records attributed to Bombina, P..

3 recordsLinked to original sources

bioRxiv2025

From Pairwise Distances to Neighborhood Preservation: Benchmarking Dimensionality Reduction Algorithms for CyTOF, scRNA-seq, and CITE-seq

Dimensionality reduction algorithms are essential tools for visualizing high-dimensional biological data, such as single-cell transcriptomics, mass cytometry by time of flight, and cellular indexing of transcriptomes and epitopes. These algorithms map complex, high-dimensional data into lower dimensions to reveal underlying structures and patterns. Despite the popularity of dimensionality reduction methods like t-SNE and UMAP, concerns have arisen regarding their ability to preserve critical aspects of high-dimensional data and their sensitivity to user-defined parameters. This study aims to evaluate the impact of extreme dimension reduction from hundreds or thousands of dimensions to just two dimensions, highlighting the resulting distortions to deeply understand their implications. Given the significance of dimensionality reduction in biological research, careful evaluation of these methods is necessary to ensure reliable and meaningful results. In this paper, we present a comprehensive evaluation of 16 dimensionality reduction methods. Our evaluation addresses several key factors, such as the preservation of pairwise distances and local neighborhood relationships between the original high-dimensional space and the low-dimensional projections.

bioinformatics↗

bioRxiv2025

Clustering Digestive Tract Tumors Using Transcriptomic and Mutation Data

Digestive tract cancers, like most other cancers, are usually categorized based on cell or tissue of origin. Molecular clustering based on the transcriptome often produces the same classification. We developed a new method, Newmanization, to reduce underlying tissue signals from transcriptomic analysis. To test our method, we downloaded data on 1635 samples of digestive tract cancers from The Cancer Genome Atlas. The available data includes transcriptomic data, by RNA-Seq, as well as binary mutation allele frequency data by whole exome sequencing. We compared, using silhouette widths and visualization by dimension reduction plots, the effectiveness of Newmanized transcriptome and mutation data to separate digestive tract cancers. The Newmanized transcriptome clusters have clearer separation and larger average silhouette widths. Feature analysis of each cluster for Newmanized transcriptomic data and mutation data revealed that clusters determined with Newmanized data contained more mRNAs present at higher frequencies than clusters defined by mutation data. This suggests that the Newmanized method holds great potential for advancing personalized transcriptomic medicine.

bioinformatics↗

bioRxiv2023

SillyPutty: Improved clustering by optimizing the silhouette width

Unsupervised clustering is an important task in biomedical science. We developed a new clustering method, called SillyPutty, for unsupervised clustering. As test data, we generated a series of datasets using the Umpire R package. Using these datasets, we compared SillyPutty to several existing algorithms using multiple metrics (Silhouette Width, Adjusted Rand Index, Entropy, Normalized Within-group Sum of Square errors, and Perfect Classification Count). Our findings revealed that SillyPutty is a valid standalone clustering method, comparable in accuracy to the best existing methods. We also found that the combination of hierarchical clustering followed by SillyPutty has the best overall performance in terms of both accuracy and speed. AvailabilityThe SillyPutty R package has been submitted to the Comprehensive R Archive Network (CRAN). Code to perform and analyze the simulations described here can be found in a Git project hosted at https://gitlab.com/krcoombes/sillyputty.

bioinformatics↗