ArticleBriefings in bioinformatics2024
scPAS: single-cell phenotype-associated subpopulation identifier.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Spatial multi omic profiling maps hypoxia-driven pro-fibrotic SPP1Experimental hematology & oncology · 2026Article
- Single-cell phenotype-associated subpopulation identification via transfer foundation model and statistical ensemble learning.BMC biology · 2026Article
- SELL Marks an Effector-Deficient CD8Human mutation · 2026Article
- Pathway-based molecular subtyping identifies LAMP5 as an EMT-associated prognostic target in colorectal cancer.Frontiers in pharmacology · 2026Article
- Identification of glycolysis-related clusters and immune cell infiltration in hepatic fibrosis progression using machine learning models and experimental validation.Frontiers in immunology · 2025Article
- Targeting Matrix Metalloproteinase-9 to Alleviate T Cell Exhaustion and Improve Sepsis Prognosis.Research (Washington, D.C.) · 2025Article
- Adrenomedullin orchestrates treatment resistance in hepatocellular carcinoma via immune microenvironment remodeling.Frontiers in genetics · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Despite significant advancements in single-cell sequencing analysis for characterizing tissue sample heterogeneity, identifying the associations between cell subpopulations and disease phenotypes remains a challenging task. Here, we introduce scPAS, a new bioinformatics tool designed to integrate bulk data to identify phenotype-associated cell subpopulations within single-cell data. scPAS employs a network-regularized sparse regression model to quantify the association between each cell in single-cell data and a phenotype. Additionally, it estimates the significance of these associations through a permutation test, thereby identifying phenotype-associated cell subpopulations. Utilizing simulated data and various single-cell datasets from breast carcinoma, ovarian cancer, and atherosclerosis, as well as spatial transcriptomics data from multiple cancers, we demonstrated the accuracy, flexibility, and broad applicability of scPAS. Evaluations on large datasets revealed that scPAS exhibits superior operational efficiency compared to other methods. The open-source scPAS R package is available at GitHub website: https://github.com/aiminXie/scPAS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.