Kevin R. Coombes

Evaluating the Robustness of Path-Preservation Benchmarks for Dimensionality Reduction Across Point-Density Thresholds: Linear and Cyclic Single-Cell Trajectories

This paper evaluates how dimensionality reduction methods affect trajectory geometry by testing their path-preservation across different point-density thresholds on linear and cyclic single-cell datasets.

Differential correlation across subpopulations of single cells in subtypes of acute myeloid leukemia

The paper introduces a method to detect differential correlations between protein pairs across subpopulations in AML samples, revealing nonlinear relationships in cytometry data.

Context-dependent correlations mislead transcriptomic network inference in bulk and single-cell data

The paper shows that pooled correlations in transcriptomic data often reverse sign when analyzed within specific contexts, highlighting the need for context-aware reporting of correlations.

Tissue-aware elastic net decomposition reveals shared and lineage-specific drug response biomarkers

The paper introduces DSEN, a tissue-aware regression method that improves drug response prediction by decomposing models into shared and lineage-specific components, achieving better performance and interpretability.

scDIG: An R Shiny Application for Interactive Density-Based Gating of Single-Cell Proteomic and Transcriptomic Data

scDIG is an R Shiny tool for interactive density-based gating of single-cell proteomic and transcriptomic data, resolving CD4+ T cell subpopulations in CITE-seq data.

Clustering Digestive Tract Tumors Using Transcriptomic and Mutation Data

The paper develops a method called Newmanization to reduce tissue signals in transcriptomic data and applies it to digestive tract cancer samples, showing improved clustering and separation compared to mutation data.

Widespread data leakage inflates accuracy and corrupts biomarker discovery in cancer drug response prediction

The paper shows that data leakage in drug response prediction models inflates accuracy and corrupts biomarker discovery, and provides a solution to avoid this issue.

A shape-constrained regression and wild bootstrap framework for reproducible drug synergy testing

SIR improves drug synergy testing by using shape-constrained regression and bootstrap methods to provide reliable statistical inference and error control.

WayFindR: Investigating Feedback in Biological Pathways

WayFindR converts pathway data into graph structures to analyze feedback loops, revealing their underrepresentation in curated databases.

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

This study evaluates 16 dimensionality reduction methods for CyTOF, scRNA-seq, and CITE-seq data, focusing on their ability to preserve pairwise distances and local neighborhoods.

Transcriptome Complexity Disentangled: A Regulatory Molecules Approach

This study identifies a small set of transcription factors and microRNAs that can predict genome-wide gene expression across multiple cancer types with high accuracy.

Benchmarking Long-read Sequencing Tools for Chromosome End-specific Telomere Analysis

This study benchmarks two long-read sequencing tools for telomere length analysis, finding Telogator has higher sensitivity while TECAT shows better accuracy and computational efficiency.

plasma: Partial LeAst Squares for Multiomics Analysis

plasma is a supervised multiomics method that integrates features into biologically relevant components to predict patient outcomes, outperforming unsupervised and single-omics models.

Topological Structures in the Space of Treatment-Naive Patients With Chronic Lymphocytic Leukemia

The paper applies topological data analysis to clinical data from CLL patients to identify complex structures like loops and voids, revealing prognostically important factors.

SillyPutty: Improved clustering by optimizing the silhouette width

SillyPutty improves clustering by optimizing silhouette width, showing comparable accuracy to existing methods and better performance when combined with hierarchical clustering.

Diverse tumorigenic consequences of human papillomavirus integration in primary oropharyngeal cancers

This study identifies HPV integration hotspots in oropharyngeal cancers and shows their role in disrupting host gene expression and genomic stability.

RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

RCytoGPS converts JSON karyotype data into R objects for analysis and visualization, streamlining computational cytogenetic pathology.

Oncogenetic Network Estimation with Disjunctive Bayesian Networks

This paper introduces a Bayesian network model for oncogenetic network estimation, showing its ability to infer progression networks from synthetic and real data.

SITH: an R package for visualizing and analyzing a spatial model of intratumor heterogeneity

SITH is an R package that models tumor growth and mutation spatially, generates synthetic sequencing data, and provides 3D visualizations of intratumor heterogeneity.

Identification and comparison of genes differentially regulated by transcription factors and miRNAs

The paper identifies genes differentially regulated by transcription factors and miRNAs using TCGA data and linear models to compare their regulatory effects.

Mercator: An R Package for Visualization ofDistance Matrices

Mercator is an R package that visualizes distance matrices using multiple algorithms to help researchers compare different distance metrics in unsupervised data analysis.

CytoGPS: A Web-Enabled Karyotype Analysis Tool for Cytogenetics

CytoGPS converts ISCN karyotype data into a machine-readable format and binary LGF model for downstream computational analysis.

Malachite: A Gene Enrichment Meta-Analysis (GEM) Tool for ToppGene

Malachite automates gene enrichment meta-analyses across multiple datasets using ToppGene, identifying xenobiotic stimulus and NADP activity as enriched processes in smoking-related gene expression data.

Polychrome: Creating and Assessing Qualitative Palettes With Many Colors

Polychrome creates and evaluates color palettes with at least 24 distinguishable colors for categorical data visualization, addressing limitations in existing R packages.

Decomposing the Apoptosis Pathway Into Biologically Interpretable Principal Components

The paper improves PCA for biological data by automating a graphical Bayesian method to identify significant principal components with biological interpretations, applied to proteomics data from acute myeloid leukemia patients.