DeepDeconUQ estimates malignant cell fraction prediction intervals in bulk RNA-seq tissue

Accurate estimation of malignant cell fractions in tissues plays a critical role in cancer diagnosis, prognosis, and subsequent treatment decisions. However, most currently available methods provide only point estimates, neglecting the quantification of uncertainties, which is essential for both cli...

Full description

Saved in:
Bibliographic Details
Published in:PLoS computational biology Vol. 21; no. 6; p. e1013133
Main Authors: Huang, Jiawei, Du, Yuxuan, Kelly, Kevin R., Lv, Jinchi, Fan, Yingying, Zhong, Jiang F., Sun, Fengzhu
Format: Journal Article
Language:English
Published: United States Public Library of Science 04.06.2025
Public Library of Science (PLoS)
Subjects:
Accuracy
Artificial neural networks
Biology and Life Sciences
Calibration
Cancer
Computational Biology - methods
Computer and Information Sciences
Conformity
Datasets
Deep Learning
Estimates
Gene expression
Gene sequencing
Genetic aspects
Humans
Intervals
Lower bounds
Medical research
Medicine and Health Sciences
Methods
Neoplasms - genetics
Neoplasms - pathology
Neural networks
Neural Networks, Computer
Performance evaluation
Predictions
Quantiles
Research and analysis methods
Ribonucleic acid
RNA
RNA sequencing
RNA-Seq - methods
Sequence Analysis, RNA - methods
Single-Cell Analysis - methods
Statistical analysis
Uncertainty
Validity
United States
ISSN:1553-7358, 1553-734X, 1553-7358
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Accurate estimation of malignant cell fractions in tissues plays a critical role in cancer diagnosis, prognosis, and subsequent treatment decisions. However, most currently available methods provide only point estimates, neglecting the quantification of uncertainties, which is essential for both clinical and research applications. This study introduces DeepDeconUQ, a deep neural network model developed to estimate prediction intervals for malignant cell fractions based on bulk RNA-seq data. This approach addresses limitations in current malignant cell fraction estimation methods by integrating uncertainty quantification into predictions of cancer cell fractions. DeepDeconUQ leverages single-cell RNA sequencing (scRNA-seq) data in conjunction with conformalized quantile regression to produce reliable prediction intervals. The model trains a quantile regression neural network to establish upper and lower bounds for cancer cell proportions, followed by a calibration step that refines these intervals to ensure both statistical validity (coverage probability) and discrimination (narrow intervals). Benchmark analyses indicate that DeepDeconUQ consistently surpasses existing methods, achieving high coverage accuracy with tight prediction intervals across simulated and real cancer datasets. The robustness of DeepDeconUQ is further demonstrated by its resilience to various gene expression perturbations. The DeepDeconUQ method is publicly accessible at https://github.com/jiaweih14/DeepDeconUQ .
Bibliography:new_version
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
The authors have declared that no competing interests exist.
Lead Contact.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1013133