Joint representation of spliced and unspliced RNA expands information content in single-cell expression data
Year: 2026
Type: article
Abstract: Motivation: Single-cell RNA-seq captures both mature (spliced) and nascent (unspliced) transcripts, yet standard preprocessing collapses these signals into a single expression matrix, obscuring recoverable structure and introducing splicing-dependent artifacts in downstream analysis. We asked whether treating spliced and unspliced counts as distinct feature sets increases recoverable latent structure in unsupervised dimension reduction. Results: Across four datasets, we applied non-negative matrix factorization to spliced-only, unspliced-only, added, and concatenated representations, estimating the revealable rank, the largest stable rank achieving near-optimal held-out error, by cross-validation. Concatenated representations exceeded the added representation in all four datasets and surpassed both single-modality inputs in three of four, indicating that preserving splicing status exposes additional stable latent programs beyond either modality alone or their sum. Minimal overlap between spliced- and unspliced-derived factors corresponded to modality-specific gene set enrichment 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. Availability and Implementation: The 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.
Source: Bioinformatics Advances
Authors: Arya Jadhav, Zachary J DeBruine
Institution: Grand Valley State University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,726
Funder: Chan Zuckerberg Initiative
Award: G7400599451
OpenAlex ID: W7223292990