scTACL: A multitask topology-aware contrastive learning approach for single-cell transcriptomics analysis

Clicks: 1
ID: 315865
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

Ranked #390 of 825 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 825 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 The advent of single-cell RNA sequencing (scRNA-seq) technology has allowed researchers to measure gene expression profiles at the single-cell level, providing valuable insights into cellular heterogeneity. However, due to the limitations of current sequencing platforms, scRNA-seq data often contain significant noise, particularly severe dropout events, which pose major challenges for subsequent analyses. Results In this study, we developed a new method called scTACL. This approach uses contrastive learning between a cell similarity graph and a cell embedding similarity graph, employing a zero-inflated negative binomial (ZINB) distribution to model the reconstructed data. This alignment helps the processed data better reflect true biological signals. It delivers superior results in key tasks such as data imputation, clustering, batch effect correction, and cell-cell interaction. Additionally, scTACL successfully identified two distinct subtypes of epithelial cells in lung adenocarcinoma tissues, further demonstrating its effectiveness and usefulness in complex biological settings. Notably, without relying on spatial location information, scTACL still effectively distinguished the epithelial and mesenchymal regions in the spatial transcriptome data of liver cancer and identified the COLLAGEN signaling pathway, which plays a crucial role in the epithelial-mesenchymal transition process through intercellular communication analysis. Availability The benchmarking datasets used in this study are available in https://figshare.com/s/e3b0ca6b8e3d4a619d5f . The source code for scTACL is available on GitHub at https://github.com/doriszmr/scTACL. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7163577133 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Murong Zhou, Xin Lü, Yingjian Liang, Alfred Wei Chieh Kow, G Wang, Qiaoming Liu, Yuming Zhao
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag361
URL
https://doi.org/10.1093/bioinformatics/btag361
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.