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FPCAM: A Weighted Dictionary-Driven Model for Single-Cell Annotation in Pulmonary Fibrosis
by
, , ,
Guojun Liu
1,2
,
Yan Shi
1,
Hongxu Huang
1,
Ningkun Xiao
3,
Chuncheng Liu
1,2,
Hongyu Zhao
1,2,
Yongqiang Xing
1,2 and
Lu Cai
1,2,* 1
School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou 014000, China
2
Inner Mongolia Key Laboratory of Life Health and Bioinformatics, Inner Mongolia University of Science and Technology, Baotou 014000, China
3
Department of Immunochemistry, Institution of Chemical Engineering, Ural Federal University, Yekaterinburg 620000, Russia
*
Author to whom correspondence should be addressed.
Biology 2025, 14(5), 479; https://doi.org/10.3390/biology14050479 (registering DOI)
Submission received: 29 March 2025
/
Revised: 23 April 2025
/
Accepted: 25 April 2025
/
Published: 26 April 2025
(This article belongs to the Special Issue Computational Discovery Tools in Genomics and Precision Medicine)
Simple Summary
Identifying which cell types are involved in disease is crucial for developing better treatments. New single-cell RNA sequencing technologies allow researchers to study individual cells in great detail, but accurately labeling these cells remains difficult. Many current tools rely on prior knowledge, which can lead to errors. We developed FPCAM, a fully automated tool that accurately identifies lung-related cell types, especially in diseases like pulmonary fibrosis. FPCAM uses marker gene patterns and a curated reference to make reliable predictions. It outperformed several popular tools and accurately identified specific cell subtypes in a separate dataset. FPCAM is freely available and easy to access online.
Abstract
The groundbreaking development of scRNA-seq has significantly improved cellular resolution. However, accurate cell-type annotation remains a major challenge. Existing annotation tools are often limited by their reliance on reference datasets, the heterogeneity of marker genes, and subjective biases introduced through manual intervention, all of which impact annotation accuracy and reliability. To address these limitations, we developed FPCAM, a fully automated pulmonary fibrosis cell-type annotation model. Built on the R Shiny platform, FPCAM utilizes a matrix of up-regulated marker genes and a manually curated gene–cell association dictionary specific to pulmonary fibrosis. It achieves accurate and efficient cell-type annotation through similarity matrix construction and optimized matching algorithms. To evaluate its performance, we compared FPCAM with state-of-the-art annotation models, including SCSA, SingleR, and SciBet. The results showed that FPCAM and SCSA both achieved an accuracy of 89.7%, outperforming SingleR and SciBet. Furthermore, FPCAM demonstrated high accuracy in annotating the external validation dataset GSE135893, successfully identifying multiple cell subtypes. In summary, FPCAM provides an efficient, flexible, and accurate solution for cell-type identification and serves as a powerful tool for scRNA-seq research in pulmonary fibrosis and other related diseases.
Keywords:
cell annotation; scRNA-seq; FPCAM; cell–gene association dictionary
