Joint representation of spliced and unspliced RNA expands information content in single-cell expression data.
- 1Department of Information Sciences and Technology, Grand Valley State University, Grand Rapids, MI 49504, United States.
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.
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BibTeX
@article{jadhav2026joint,
title = {{Joint representation of spliced and unspliced RNA expands information content in single-cell expression data.}},
author = {Arya Jadhav and Zachary J DeBruine},
journal = {Bioinformatics advances},
year = {2026},
month = oct,
doi = {10.1093/bioadv/vbag263},
url = {https://europepmc.org/article/MED/42960177},
note = {CC BY},
}
RIS
TY - JOUR
TI - Joint representation of spliced and unspliced RNA expands information content in single-cell expression data.
AU - Arya Jadhav
AU - Zachary J DeBruine
PY - 2026
DA - 2026/10/22
JO - Bioinformatics advances
DO - 10.1093/bioadv/vbag263
UR - https://europepmc.org/article/MED/42960177
AB - 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.
SN - 2635-0041
ER -