A latent factor framework to organize regulatory and metabolic programs inferred from scRNA-seq
Clicks: 2
ID: 319259
2026
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
0.3
/100
2 views
1 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #73 of 104 articles by views in Bioinformatics advances
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Single-cell RNA sequencing enables high-resolution characterization of transcriptional heterogeneity, but provides only a partial view of the regulatory and metabolic processes associated with cellular states. Several computational methods infer transcription factor (TF) activity and metabolic features directly from RNA, yielding complementary functional representations of cellular organisation. Here, we use a latent factor organisational strategy to jointly model four transcriptome-derived functional projections—gene expression, TF regulon activity, metabolite-level features and predicted metabolic fluxes. Although all layers originate from the same measurement, each captures distinct regulatory or metabolic programs. The resulting latent space organizes these inferred programs into coordinated axes of variation guided by complementary regulatory and metabolic constraints, facilitating functional interpretation beyond gene expression alone. When applied to breast cancer cell line data, the proposed framework organises transcriptome-derived signals into distinct functional programs, including proliferative, oxidative-metabolic and stress-associated axes, that are only partially resolved in RNA-only analyses. Overall, we demonstrate that regulatory and metabolic programs inferred from scRNA-seq can be structured into an interpretable latent representation, supporting a more coherent functional characterization of cellular states from transcriptome-derived functional projections.
| Reference Key |
openalex_W7166864273
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Chiara Napoli, Francesco Bardozzo, Suraj Verma, Le Minh Thao Doan, Pierpaolo Fiore, Carmen Faggiano, Claudio Angione, Annalisa Occhipinti, Roberto Tagliaferri |
| Journal | Bioinformatics advances |
| Year | 2026 |
| DOI |
10.1093/bioadv/vbag185
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.