Biology subjects

DeBruine, Z. J.

Publications and source records attributed to DeBruine, Z. J..

4 recordsLinked to original sources

bioRxiv2025

Scalable Non-negative Matrix Factorization of the Human Cell Census Reveals Interpretable Transcriptional Programs

Large single-cell atlases now span tens of millions of cells, yet few provide reusable and interpretable reference representations that support direct biological reasoning at atlas-scale. Here, we present an interpretable Non-negative Matrix Factorization reference embedding of 28.5 million healthy cells and approximately 60,000 genes from the Human Cell Census. The resulting gene and cell weights define additive transcriptional programs that align with annotated cell types and organized biological pathways. New datasets can be projected into this fixed reference space without fine-tuning or retraining, as we demonstrate using an independent cystic fibrosis dataset. This resource provides a transparent coordinate system for exploratory analysis and hypothesis generation, complementing deep embeddings that prioritize integration or prediction with a representation designed for interpretability and reuse.

bioinformatics↗

bioRxiv2025

Joint representation of spliced and unspliced RNA expands information content in single-cell expression data

MotivationSingle-cell RNA-seq captures both mature (spliced) and nascent (unspliced) transcripts, yet standard preprocessing typically collapses these signals into a single expression matrix, obscuring recoverable transcriptional structure and introducing splicing-dependent artifacts in downstream embeddings in foundation models. We asked whether treating spliced and unspliced counts as distinct feature sets increases recoverable latent structure in unsupervised dimension reduction. ResultsAcross four datasets with paired spliced and unspliced count layers, we applied non-negative matrix factorization (NMF) to spliced-only, unspliced-only, summed, and concatenated representations. Using cross-validation, we estimated the revealable rank k* (largest stable rank achieving near-optimal held-out error). In all datasets, concatenated representations consistently supported higher k* than spliced-only or unspliced-only inputs and typically exceeded the summed representation, indicating that preserving splicing status exposes additional stable latent programs. Minimal overlap between spliced- and unspliced-derived factors corresponded to modality-specific gene set enrichment patterns consistent with complementary steady-state and nascent regulatory activity. These results establish splicing status as an informative axis of variation and motivate preserving spliced and unspliced layers as first-class features to maximize information recovery in single-cell analysis. Availability and ImplementationThe singlet R package is available at github.com/zdebruine/singlet. Code to reproduce all figures is available at github.com/Arya-86/nmf-spliced-unspliced. Supplementary informationSupplementary data are appended to this manuscript.

bioinformatics↗

bioRxiv2024

EZH2-driven immune evasion defines high-risk pediatric AML with t(16;21) FUS::ERG gene fusion.

The past 25 years of clinical trials have produced few improvements in pediatric AML (pAML) outcomes. This is acutely evident in patients with t(16;21)(p11;q22), yielding FUS::ERG. Patients with FUS::ERG-positive AML relapse quickly and do not respond to transplantation. Major histocompatibility complex (MHC) class I & II receptors and costimulatory molecules are absent at diagnosis in FUS::ERG-positive AML, mirroring the phenotype and outcomes of post-transplant relapse. We show that this is driven by overexpression of EZH2, in vitro and in multiple clinical cohorts. While FUS::ERG AML is the most extreme example, this phenotype is shared by lethal CBFA2T3::GLIS2-driven AML, and patients with RUNX1::RUNX1T1 have significantly worse outcomes when EZH2 overexpression co-occurs. The FDA-approved EZH2 inhibitor tazemetostat reverses this phenotype, re-establishes MHC presentation, and elicits immune effector cell-mediated elimination. EZH2 inhibitors may provide the first targeted therapeutic frontline option for AML patients with FUS::ERG, with the potential for broader frontline immunostimulatory benefits. STATEMENT OF SIGNIFICANCEHere we show an immune-evasive phenotype, present at diagnosis and characterized by elevated EZH2 levels and loss of MHC class I and II, defines a high-risk subtype of acute leukemia. Treatment with the EZH2 inhibitor tazemetostat and IFN-{gamma} reverses this phenotype and results in immune cell engagement and blast elimination.

cancer biology↗

bioRxiv2021

Fast and robust non-negative matrix factorization for single-cell experiments

Non-negative matrix factorization (NMF) is a popular method for analyzing strictly positive data due to its relatively straightforward interpretation. However, NMF has a reputation as a less efficient alternative to the singular value decomposition (SVD), a standard operation that is highly optimized in most linear algebra libraries. Sparse single-cell sequencing assays, now feasible in thousands of subjects and millions of cells, generate data matrices with tens of thousands of strictly non-negative transcript abundance entries. We present an extremely fast NMF implementation made available in the RcppML (Rcpp Machine Learning library) R package that rivals the runtimes of state-of-the-art Singular Value Decomposition (SVD). NMF can now be run quickly on desktop computers to analyze sparse single-cell datasets consisting of hundreds of thousands of cells. Our method improves upon current NMF implementations by introducing a scaling diagonal to increase interpretability, guarantee consistent regularization penalties across different random initializations, and symmetry in symmetric factorizations. We use our method to show how NMF models learned on standard log-normalized count data are interpretable dimensional reductions, describe interpretable patterns of coordinated gene activities, and explain biologically relevant metadata. We believe NMF has the potential to replace PCA in most single-cell analyses, and the presented NMF implementation overcomes previous challenges with long runtime.

bioinformatics↗