Faculty, Staff and Student Publications
Language
English
Publication Date
2-1-2026
Journal
Advanced Science
DOI
10.1002/advs.202514502
PMID
41309494
PMCID
PMC12884779
PubMedCentral® Posted Date
11-27-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Single-cell RNA sequencing (scRNA-seq) is a powerful technique for understanding cellular diversity, but processing large patient cohorts to identify phenotype-associated cell populations remains challenging. Here, scPER (Estimating cell Proportions using single-cell RNA-seq Reference), a rigorous approach combining adversarial autoencoder and extreme gradient boosting to estimate tumor microenvironment cell compositions and identify phenotype-associated subclusters for bulk RNA-seq samples. Integrating scRNA-seq datasets from diverse studies, scPER constructed comprehensive reference panels and disentangled confounders from true signals. scPER achieved superior accuracy in cellular proportion estimation compared to CIBERSORTx, BayesPrism, Scaden, MuSiC, SCDC, DeSide and ReCIDE. It showed high accuracy in predicting metastatic melanoma immunotherapy response and identified a critical T cell subcluster expressing FCRL3 and SLAMF7. In metastatic urothelial cancer, scPER predicted TGFβ-mediated inhibition of CD4 naïve T cells to diminish PD-L1 checkpoint blockade efficacy. scPER enables robust integration of scRNA-seq datasets to estimate cellular proportions across tumors and identify clinically relevant cell populations.
Keywords
Humans, Sequence Analysis, RNA, Single-Cell Analysis, Phenotype, Tumor Microenvironment, Computational Biology, Neoplasms, Melanoma, RNA-Seq, cancer biology, deconvolution, machine learning, tumor microenvironment
Published Open-Access
yes
Recommended Citation
, "scPER: A Rigorous Computational Approach to Determine Cellular Subtypes in Tumors Aligned With Cancer Phenotypes From Total RNA Sequencing" (2026). Faculty, Staff and Student Publications. 812.
https://digitalcommons.library.tmc.edu/uthshis_docs/812