Biology subjects

Coombes, K. R.

Publications and source records attributed to Coombes, K. R..

At least 19 recordsLinked to original sources

bioRxiv2026

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

Motivation: Comparative studies of trajectory inference (TI) methods evaluate complete computational pipelines, making it impossible to isolate how much distortion is introduced specifically by the dimensionality reduction (DR) step. To our knowledge, no study has directly and systematically evaluated how well DR methods alone preserve a known reference path when projecting high-dimensional single-cell data to two dimensions, and no current study has introduced a dedicated set of metrics to quantify the degree of path-preservation quality after dimensionality reduction. This gap matters because DR is a universal preprocessing choice that shapes all downstream trajectory analysis, yet its independent geometric effect on path structure remains uncharacterized, and practitioners have no principled way to quantify it. Methods: We tested a panel of candidate path-preservation metrics on two single-cell datasets with known reference trajectories, one linear and one cyclic, to determine whether the resulting metric values, DR-method rankings, and overall conclusions are sensitive to the number of points used to construct and display the path, and whether they remain stable once that choice is fixed. The primary linear dataset is a CD4+ T-cell surface-protein dataset (3,096 cells, 51 proteins); a ground-truth reference path was constructed from cells lying close to the first principal component (PC1) of a single cluster, providing a known linear trajectory in the high-dimensional space. Sixteen DR methods were applied and twelve geometric path-preservation metrics were computed, spanning log-ratio distortions of length, curvature, and spatial similarity; Spearman rank correlations of pairwise distances and segment lengths; and structural complexity measures including self-intersection frequency and coiling. To test the sensitivity of this evaluation framework to path density, we varied the fraction of cells used to define the reference path from 1% to 10% (31-310 path points) and tracked how method rankings responded. The same analysis was repeated on a topologically distinct reference, a closed B-cell cell-cycle loop detected by persistent homology in a separate CyTOF dataset, to test whether these conclusions about metric and method stability hold for cyclic as well as linear trajectories. Results: The central sensitivity question, whether the number of points used to construct the reference path changes the evaluation's conclusions, was answered negatively on both datasets. On the linear PC1 trajectory, absolute values of all twelve metrics shifted smoothly as the path-density threshold was varied from 1% to 10% (31-310 points), reflecting the broadening of the reference band, but each method's composite rank remained stable across every threshold: no method changed performance tier as the hyperparameter varied. A composite rank aggregating all twelve metrics identified the same consistently high-performing methods (UMAP, MDS, CNPE, TSNE, SPE, LPMIP) and consistently low-performing methods (SPMDS, LPP, DVE, LAPEIG, PHATE) at every density level tested. Considered on its own, `SpatDistSpear`, the single most discriminating metric, separated a high-fidelity group (LPMIP, DM, MDS, SPMDS, DVE, CISOMAP, CNPE; all r > 0.80) from a mid-range group (LAPEIG, SPE, PHATE, UMAP, TSNE, FOSMOD) and a low-fidelity group (PFA, NNP, LPP); global distance preservation and overall composite performance therefore do not always agree on the same "top tier" of methods, but this disagreement in which metric identifies the best methods is itself density-independent rather than an artifact of the specific threshold chosen. The cyclic loop reproduced the same density-independence: absolute metric values drifted with the per-segment band width, yet each method's composite rank again held constant across all eleven density levels. The identity of the best and worst performers was largely, though not entirely, conserved between the two topologies, with CNPE, LPMIP, SPE, and MDS as top performers and LAPEIG, DVE, and SPMDS as poor performers on both the linear path and the closed loop. UMAP and TSNE were exceptions, dropping from top performers on the linear path to the middle of the sixteen-method panel, rather than the worst tier, on the closed loop. This topology-dependence is a property of the reference geometry rather than of path density: it holds consistently regardless of how many points are used to define the path. Significance: This work introduces a direct, pipeline-independent evaluation of how DR methods distort trajectory geometry, a benchmarking dimension absent from existing TI comparisons. The within-dataset rank stability result, demonstrated on both a linear and a cyclic reference trajectory, validates the use of a fixed reference-path threshold as a robust operating point for large-scale DR benchmarking; however, the partial reordering of top performers between topologies shows that a method's DR benchmark ranking is trajectory-shape-dependent and should not be assumed to transfer from a linear to a cyclic reference.

bioinformatics↗

bioRxiv2026

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

BackgroundCorrelation is the dominant input to co-expression module discovery and miRNA-target inference. Both rely on an implicit assumption: a Pearson coefficient pooled across heterogeneous samples, whether tissues, cancer types, or cell types, estimates one biologically meaningful quantity. Simpsons paradox makes this assumption fragile in principle, since between- group mean shifts can dominate or reverse within-group associations. How often this happens in real transcriptomic data has not been quantified. ResultsAcross 8,890 TCGA tumors from 31 cancer cohorts and 23,170,038 miRNA-mRNA pairs, 94.8% of pairs showed both positive and negative within-cohort correlations. Restricting to the high-variance domain of one million pairs, 13.3% of pooled correlations with |rglobal|[≥]0.2 reversed against the within-cohort majority at sign tolerance{varepsilon} = 0.05. Heterogeneity was the rule rather than the exception (median I2 = 0.86, IQR 0.80-0.90), and 99.5% of pairs rejected equal correlation across cohorts at FDR < 0.05. Of 692,770 experimentally validated miRTarBase v10 targets measurable in our data, only 0.9% were uniformly negative across cohorts. The pattern recurred across modalities. In GTEx, 21.0% of pooled signs disagreed with the tissue majority, and 23.5% of pairs flipped sign after tissue-mean removal. In 10x PBMC scRNA-seq, 13.1% of gene-gene correlations flipped after cell-type-mean removal; in CITE-seq, 37.9% of protein-RNA pairs flipped under a joint WNN partition of cells. Refining context reduced reversal, though by how much depended on the partition: within BRCA, 5.5% of pairs reversed under molecular PAM50 subtypes versus 0.35% under clinical IHC receptor status, and refining T cells into transcriptome-defined subtypes cut PBMC reversal from 11.8% to 0.13%. ConclusionsA single pooled correlation coefficient can invert direction relative to its within-context constituents at rates that are not negligible. Correlations should be reported with their context: the within-context distribution, a heterogeneity statistic, and a diagnostic that separates between-context mean shifts from within-context association. We provide a small R interface that computes these summaries.

bioinformatics↗

bioRxiv2026

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

MotivationComputational models that predict cancer drug response from genomic features are central to biomarker discovery, yet a recent audit found data leakage in 72% of 32 published methods, and complex models offer little interpretability while only modestly exceeding simple baselines under honest evaluation. Tissue lineage is a largely untapped source of legitimate inductive bias, but existing tissue-aware methods neither separate pan-cancer from lineage-specific signal nor report leakage-free performance. ResultsWe introduce the Data Shared Elastic Net (DSEN), a tissue-aware regression that decomposes each drugs model into a shared coefficient block common to all lineages and tissue-specific deviation blocks. Under leakage-free cross-validation across 265 drugs, 1,462 cell lines and 31 tissue lineages, DSEN improved mean squared error over a standard elastic net for 92.5% of drugs (mean 4.95%) while selecting 58% fewer stable shared features. Shared coefficients generalized to held-out tissues (59% tissue-level win rate) and recurrently recovered transferable pathway modules (p53, MAPK), whereas tissue blocks captured lineage markers such as the skin MITF /S100B program. The closest tissue-aware comparator, TG-LASSO, performed worse than the tissue-agnostic baseline (-13.8% mean MSE). Ablation shows tissue-aware modeling helps most when features are scarce, with no single modality dominating. Availability and implementationhttps://github.com/AsiaeeLab/tissue-aware-drug-response. Contactamir.asiaeetaheri@vumc.org

bioinformatics↗

bioRxiv2026

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

Delineating biologically meaningful cell populations within single-cell embedding spaces requires methods that balance expert guidance with reproducibility. We present scDIG, a Shiny-based tool that integrates bimodal index-driven feature selection, feature-weighted kernel density estimation, and interactive contour-based gating to define cell populations directly within two-dimensional projections of scRNA-seq and CITE-seq data. We applied scDIG to CITE-seq PBMC data from human subjects in the Cardiovascular Assessment Virginia (CAVA) cohort and show that it resolves transcriptionally distinct CD4+ T cell subpopulations within continuous embeddings that are not readily captured by conventional clustering approaches. These findings demonstrate the utility of scDIG for robust, reproducible classification of single-cell populations and for identifying immunologically relevant effector states. The app is freely available for non-commercial use at https://au-cbgm-shiny.augusta.edu/gating, with source code available at https://gitlab.com/pbombina/scdig.

bioinformatics↗

bioRxiv2026

WayFindR: Investigating Feedback in Biological Pathways

Understanding biological pathways requires more than static diagrams. We present WayFindR, an R package that converts pathway data from WikiPathways and KEGG into graph structures using igraph, enabling computational analysis of regulatory features such as negative feedback loops. Rooted in control theory, negative feedback is essential for system stability, yet it is often underrepresented in curated pathway data. In this study, we systematically analyzed pathway information from both databases across multiple species and found that feedback loops--particularly negative ones--are rarely captured. This gap likely reflects both biological and technical challenges. Biologically, feedback mechanisms are inherently complex and often remain uncharted due to limited experimental focus. Technically, pathway databases frequently lack standardized annotations and complete representations of regulatory interactions, especially inhibitory edges that are crucial for identifying feedback. These observations underscore the need for improved data curation and consistent annotation practices to enhance our understanding of regulatory dynamics. By bridging the gap between static pathway diagrams and dynamic systems-level insights, WayFindR enables reproducible and scalable investigation of feedback regulation in cellular networks. The WayFindR R package can be downloaded from the Comprehensive R Archive Network (CRAN) (https://cran.r-project.org/web/packages/WayFindR/index.html). The processed data along with code for download can be accessed via the GitLab repository (https://gitlab.com/krcoombes/wayfindr).

bioinformatics↗

bioRxiv2026

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

High-throughput drug combination screens require methods to identify synergistic pairs, yet widely used synergy scores lack statistical inference and can fail when parametric dose-response fits do not converge. We present SIR (Synergy via Isotonic Regression), a nonparametric framework that defines interaction as deviation from a monotone-additive null, fit by 2D isotonic regression. A degrees-of-freedom-corrected wild bootstrap yields calibrated p-values for each dose-response matrix. On DrugCombDB, SIR interaction surfaces achieve higher replicate concordance (median correlation 0.91 across 1,839 replicate pairs) than all baselines (0.53-0.74), while avoiding Loewes 20.9% and ZIPs 3.6% failure rates. The fitted surface also predicts missing wells (median holdout RMSE 0.040). By replacing heuristic scores with calibrated effect sizes and p-values, SIR enables principled hit calling and error-rate control in large screens.

bioinformatics↗

bioRxiv2026

Widespread data leakage inflates performance estimates in cancer drug response prediction

Drug response prediction models guide biomarker discovery, yet their reported accuracy depends on rigorous cross-validation (CV). We show that supervised feature screening applied to all samples before CV, a widespread practice, introduces data leakage that systematically underestimates prediction error. Across 265 drugs and 1,462 cancer cell lines, leakage-free CV raises mean squared error by 16.6% on average, with near-zero feature overlap between leaked and corrected pipelines (mean Jaccard 0.18; 36.2% of drugs share no features). Despite selecting five times more features, the leaked pipeline recovers known drug targets at nearly the same rate as the corrected pipeline, indicating that the inflated feature sets capture statistical artifacts rather than biological signal. A code-level audit of 32 published methods (2017-2024) confirms leakage in 23 (72%), spanning five distinct modes and cited over 3,000 times. The magnitude of inflation from this single leakage mode is comparable to improvement margins typically claimed over elastic net baselines, raising the possibility that some reported advances reflect evaluation artifacts. We provide a leakage taxonomy, audit guide and reference implementation for leakage-free evaluation.

bioinformatics↗

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

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

Measuring chromosome end-specific telomeres is of great importance and could help elucidate better treatment algorithms and aid in a better understanding of cancer, aging, cardiovascular disease, and neurodegenerative diseases. In this study, we present a comparison of two cutting edge long-read sequencing telomere length analysis tools, TECAT and Telogator. We perform a comprehensive bench-marking of these two tools using Telseq as the standard. Our analysis included evaluating these tools on sensitivity, accuracy, and computational efficiency using a diverse data set of 9 samples from the 1000 Genomes Project which have matched long-read and short-read sequencing data. We found that while Teloga-tor demonstrated superior sensitivity, identifying on average 31% more telomeric reads across all samples, TECAT showed better accuracy with measurements more closely aligned with established literature values and Telseq benchmarks (R{superscript 2} = 0.74 vs 0.37), and TECAT displayed better computational efficiency, com-pleting tasks approximately 41% faster. Both tools successfully mapped telomere lengths to individual chromosome arms, demonstrating unprecedented resolution for telomere length analysis. Our results provide crucial insights for researchers selecting tools for telomere length analysis and highlight the current capabilities and limitations of computational approaches in telomere biology.

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↗

bioRxiv2024

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

Patients are complex and heterogeneous; clinical data sets are complicated by noise, missing data, and the presence of mixed-type data. Using such data sets requires understanding the high-dimensional "space of patients", composed of all measurements that define all relevant phenotypes. The current state-of-the-art merely defines spatial groupings of patients using cluster analyses. Our goal is to apply topological data analysis (TDA), a new unsupervised technique, to obtain a more complete understanding of patient space. We applied TDA to a space of 266 previously untreated patients with Chronic Lymphocytic Leukemia (CLL), using the "daisy" metric to compute distances between clinical records. We found clear evidence for both loops and voids in the CLL data. To interpret these structures, we developed novel computational and graphical methods. The most persistent loop and the most persistent void can be explained using three dichotomized, prognostically important factors in CLL: IGHV somatic mutation status, beta-2 microglobulin, and Rai stage. In conclusion, patient space turns out to be richer and more complex than current models suggest. TDA could become a powerful tool in a researchers arsenal for interpreting high-dimensional data by providing novel insights into biological processes and improving our understanding of clinical and biological data sets. Simple SummaryClinical data sets incorporate continuous data like blood pressure or sodium levels, categorical data like cancer grade or stage, and binary data like sex or marital status. Measurements on an individual patient define a point in a high-dimensional space; data from many patients defines a "point cloud". The "shape" of the point cloud influences experimental design by describing patient variability. Topological data analysis (TDA) is a mathematical technique for understanding the shape of point clouds by finding "holes" that correspond to combinations of patient characteristics that are never observed. TDA results are stratified by dimension. Zero-dimensional features define patient subtypes. One-dimensional features ("loops") are analogs of the inside of a circle or a donut hole. Two-dimensional features ("voids") are analogs of the inside of a balloon. Here, we apply TDA to a clinical data set of previously untreated patients with Chronic Lymphocytic Leukemia to find loops and voids.

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↗

bioRxiv2023

Transcriptome Complexity Disentangled: A Regulatory Elements Approach

AbstractTranscription factors (TFs) and microRNAs (miRNAs) are fundamental regulators of gene expression, cell state, and biological processes. This study investigated whether a small subset of TFs and miRNAs could accurately predict genome-wide gene expression. We analyzed 8895 samples across 31 cancer types from The Cancer Genome Atlas and identified 28 miRNA and 28 TF clusters using unsupervised learning. Medoids of these clusters could differentiate tissues of origin with 92.8% accuracy, demonstrating their biological relevance. We developed Tissue-Agnostic and Tissue-Aware models to predict 20,000 gene expressions using the 56 selected medoid miRNAs and TFs. The Tissue- Aware model attained an R2 of 0.70 by incorporating tissue-specific information. Despite measuring only 1/400th of the transcriptome, the prediction accuracy was comparable to that achieved by the 1000 landmark genes. This suggests the transcriptome has an intrinsically low-dimensional structure that can be captured by a few regulatory molecules. Our approach could enable cheaper transcriptome assays and analysis of low-quality samples. It also provides insights into genes that are heavily regulated by miRNAs/TFs versus alternative mechanisms. However, model transportability was impacted by dataset discrepancies, especially in miRNA distribution. Overall, this study demonstrates the potential of a biology-guided approach for robust transcriptome representation.

bioinformatics↗

bioRxiv2023

plasma: Partial LeAst Squares for Multiomics Analysis

Recent growth in the number and applications of high-throughput "omics" technologies has created a need for better methods to integrate multiomics data. Much progress has been made in developing unsupervised methods, but supervised methods have lagged behind. We present a novel algorithm, plasma, to learn models to predict time-to-event outcomes from multiomics data sets. Plasma uses two layers of existing partial least squares algorithms to first select components that covary with the outcome and then construct a joint Cox proportional hazards model. We apply plasma to the stomach adenocarcinoma (STAD) data from The Cancer Genome Atlas. We validate the model both by splitting the STAD data into training and test sets and by applying it to the subset of esophageal cancer (ESCA) containing adenocarcinomas. We use the other half of the ESCA data, which contains squamous cell carcinomas dissimilar to STAD, as a negative control. Our model successfully separates both the STAD test set (p = 2.73 x 10-8) and the independent ESCA validation data (p = 0.025) into high risk and low risk patients. It does not separate the negative control data set (ESCA squamous cell carcinomas, p = 0.57). The performance of the joint multiomics model is superior to that of the individually trained models. It is also superior to the performance of an unsupervised method (Multi Omics Factor Analysis; MOFA) that finds latent factors to be used as putative predictors in a post-hoc survival analysis. Many of the factors that contribute strongly to the plasma model can be justified from the biological literature. SignificanceTo fill the unmet need for supervised multiomics methods, we introduce plasma, an algorithm based on partial least squares that integrates multiomics features into biologically relevant "components" that can predict patient outcomes. Availability and ImplementationThe plasma R package can be obtained from The Comprehensive R Archive Network (CRAN) athttps://CRAN.R-project.org/package=plasma. The latest version of the package can always be obtained from R-Forge at https://r-forge.r-project.org/R/?group_id=1746. Source code and data for the analysis presented here can be obtained from GitLab, at https://gitlab.com/krcoombes/plasma. ContactEmail: kcoombes@augusta.edu Supplementary InformationSupplementary material is available from the journal web site.

bioinformatics↗

bioRxiv2022

Intratumoral heterogeneity and clonal evolution induced by HPV integration

The human papillomavirus (HPV) genome is integrated into host DNA in most HPV-positive cancers, but the consequences for chromosomal integrity are unknown. Continuous long-read sequencing of oropharyngeal cancers and cancer cell lines revealed a unique form of structural variation, i.e., heterocateny, characterized by diverse, interrelated, and repetitive patterns of concatemerized virus and host DNA segments within a cancer. Unique breakpoint sequences shared across structural variants facilitated stepwise reconstruction of their evolution from a common molecular ancestor. This analysis revealed that virus and virus-host concatemers are unstable and, upon insertion into and excision from chromosomes, facilitate capture, amplification, and recombination of host DNA and chromosomal rearrangements. Evidence of heterocateny was detected in extrachromosomal and intrachromosomal DNA. The data indicate that heterocateny is driven by the dynamic, aberrant replication and recombination of an oncogenic DNA virus, thereby extending known consequences of HPV integration to include promotion of intratumoral heterogeneity and clonal evolution.

cancer biology↗

bioRxiv2022

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

Mass cytometers can record 40-50 parameters per single cell for millions of cells in a sample, and in particular, for leukemic cells. Many methods have been developed to cluster phenotypically similar cells within cytometry data, but there are fewer methods to visualize activity and interactions of pairs of proteins across these populations. We have developed a workflow for analyzing correlations associated with predfined populations. By clustering blood samples from acute myeloid leukemia (AML) patients and normal controls using an established algorithm, we obtained a minimum spanning tree of clusters of single cells. Using surface marker expression, we identified clusters on the tree that belonged to phenotypes of interest. Next, we computed correlations between pairs of proteins in each cluster. We developed a novel, coherent, probability-based statistic to test differences between vectors of correlation coefficients. By comparing all combinations of the normal controls under the statistic, we created an empirical distribution that provided a conservative measure of differential correlation. Using this empirically-derived distribution to define significance, we compared pooled samples from AML subtypes and normal controls to detect differential correlations. Given the structure present within this cytometry data set, we found it natural to consider correlations in this manner versus aggregating all data and computing a single correlation. Our results have the advantage that we can localize the statistical measure to determine contributions from particular phenotypic populations. Differentially correlated pairs of proteins can be further explored by considering a populations distribution of correlation coefficients or biaxially plotting protein expressions within individual cells in a given population. Our approach leads to a better understanding of the nonlinear relationships that exist in the cytometry data. Author summaryWe introduce a novel method for analyzing the abundance of single cell data collected by high-throughput technologies. Due to the high dimensionality of such datasets, there is a need for methods to identify significant interactions between genes or proteins. In particular, we are interested in statistical differences between correlations of proteins within populations of cells determined by traditional immunophenotyping techniques. In this paper, we have demonstrated the utility of this new framework in the case of blood samples from individuals with different subtypes of leukemia and compare to healthy controls. The motivation for this application is that differences between can illuminate potential drivers of the disease. We have illustrated an example of how intracellular events can be detected by our statistic. Finally, this method is flexible in that it can also be applied in a variety of contexts where there are vectors of correlation coefficients that need to be compared.

systems biology↗

bioRxiv2021

Diverse tumorigenic consequences of human papillomavirus integration in primary oropharyngeal cancers

Human papillomavirus (HPV) causes 5% of all cancers and frequently integrates into host chromosomes, but the impacts of integration in tumorigenesis remain unclear. Analysis of 105 HPV-positive oropharyngeal cancers by whole genome sequencing detects viral integration in 77%, revealing five statistically significant integration hotspots near genes that regulate epithelial stem cell maintenance (i.e. SOX2, TP63, FGFR, MYC) and immune evasion (i.e. CD274). Somatic hyperamplification is enriched 16-fold near HPV integrants, and the extent of focal host genomic instability increases with local density of HPV integrants. Genes expressed at extreme outlier levels are increased 86-fold within +/- 150 kb of integrants. Across 95% of tumors with integration, host gene transcription is disrupted via intragenic integrants, chimeric transcription, outlier expression, gene breaking and/or de novo expression of noncoding or imprinted genes. We conclude that HPV integration contributes substantively to cancer development by causing extensive disruption of host genome structure and gene expression.

genomics↗

bioRxiv2021

RCytoGPS: An R Package for Reading and Visualizing Cytogenetics Data

SummaryCytogenetics data, or karyotypes, are among the most common clinically used forms of genetic data. Karyotypes are stored as standardized text strings using the International System for Human Cytogenomic Nomenclature (ISCN). Historically, these data have not been used in large-scale computational analyses due to limitations in the ISCN text format and structure. Recently developed computational tools such as CytoGPS have enabled large-scale computational analyses of karyotypes. To further enable such analyses, we have now developed RCytoGPS, an R package that takes JSON files generated from CytoGPS.org and converts them into objects in R. This conversion facilitates the analysis and visualizations of karyotype data. In effect this tool streamlines the process of performing large-scale karyotype analyses, thus advancing the field of computational cytogenetic pathology. Availability and ImplementationFreely available at https://CRAN.R-project.org/package=RCytoGPS Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗