Biology subjects

McGee, R. L.

Publications and source records attributed to McGee, R. L..

5 recordsLinked to original sources

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

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↗

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↗

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↗

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↗