Starlng: STability Analysis of coReguLated "Nests" of Genes

Clicks: 9
ID: 337620
2026
Free PDF
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.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #969 of 983 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 983 in total.

Mint this article as an NFT
Not yet minted

Create 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
Sign in to mint
Abstract
Abstract Motivation Single-cell assays (both single and multi-omics) represent the state-of-the-art for detailed characterisation of cell phenotypes, a task frequently initialised with the identification of groups of genes linked to well-defined, stable subpopulations of cells. Gene expression covariation patterns are simplified precursors of Gene Regulatory Networks (GRNs), and play a crucial role in inferring causality and dynamics within GRNs. However, current gene clustering methods struggle to capture spatial organisation and relationship dynamics along developmental trajectories, while remaining vulnerable to noise and algorithmic stochasticity. Results Expanding on the ClustAssess framework, which shifted the cluster-centric assessment of populations of cells to an element-centric perspective, we propose to assess the covariation induced by gene graph clustering. The Starlng (abbrev) framework provides an optimised, interactive environment for the identification of the filtered modules. The pipeline identifies the stable configuration of clustering parameters and robust gene modules (nests of genes), followed by the removal of the outlier clusters based on the pseudotime variance. We illustrate Starlng on the identification of reliable, reproducible communities of genes, as well as reveal unbiased hierarchical relationships between higher-level cell clusters and more granular subgroups of genes. Availability Starlng is implemented in R and freely available at https://github.com/Core-Bioinformatics/Starlng . Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7224114348 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Andi Munteanu
Are you one of these authors? Claim your profile
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag801
URL
https://doi.org/10.1093/bioinformatics/btag801
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.