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...

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Vydáno v:	PLoS computational biology Ročník 21; číslo 6; s. e1013133
Hlavní autoři:	Huang, Jiawei, Du, Yuxuan, Kelly, Kevin R., Lv, Jinchi, Fan, Yingying, Zhong, Jiang F., Sun, Fengzhu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 04.06.2025 Public Library of Science (PLoS)
Témata:	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
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Abstract	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 .
AbstractList	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. 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.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. 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 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. Accurately determining the proportion of malignant cells in tumor tissues is crucial for cancer diagnosis and treatment planning. Current methods often provide single estimates without indicating the uncertainty, which can lead to overconfidence in clinical decisions. Here, we present DeepDeconUQ, a deep learning tool that not only predicts the fraction of malignant cells in bulk RNA sequencing data but also quantifies the uncertainty around these estimates. By leveraging single-cell RNA sequencing data to simulate realistic tumor samples, DeepDeconUQ trains a neural network to generate prediction intervals—ranges within which the true malignant cell fraction is likely to lie with high probability. This approach combines quantile regression and statistical calibration to ensure reliability without restrictive assumptions about data distribution. When tested on both simulated and real-world datasets, DeepDeconUQ consistently outperformed existing methods, delivering precise intervals that reliably capture true values while remaining robust against technical noise in gene expression measurements. Our tool addresses a critical gap in cancer genomics by providing clinicians and researchers with confidence intervals that enhance the interpretability of bulk tissue analyses. This advancement could improve personalized treatment strategies and reduce errors in downstream research applications.
Audience	Academic
Author	Kelly, Kevin R. Lv, Jinchi Du, Yuxuan Zhong, Jiang F. Huang, Jiawei Fan, Yingying Sun, Fengzhu
AuthorAffiliation	2 Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America Tsinghua University, CHINA 1 Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America 4 Data Sciences and Operations Department, University of Southern California, Los Angeles, California, United States of America 3 Division of Hematology, University of Southern California, Los Angeles, California, United States of America 5 Department of Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, California, United States of America
AuthorAffiliation_xml	– name: 4 Data Sciences and Operations Department, University of Southern California, Los Angeles, California, United States of America – name: 3 Division of Hematology, University of Southern California, Los Angeles, California, United States of America – name: 1 Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America – name: Tsinghua University, CHINA – name: 5 Department of Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, California, United States of America – name: 2 Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, Texas, United States of America
Author_xml	– sequence: 1 givenname: Jiawei surname: Huang fullname: Huang, Jiawei – sequence: 2 givenname: Yuxuan surname: Du fullname: Du, Yuxuan – sequence: 3 givenname: Kevin R. surname: Kelly fullname: Kelly, Kevin R. – sequence: 4 givenname: Jinchi surname: Lv fullname: Lv, Jinchi – sequence: 5 givenname: Yingying surname: Fan fullname: Fan, Yingying – sequence: 6 givenname: Jiang F. surname: Zhong fullname: Zhong, Jiang F. – sequence: 7 givenname: Fengzhu orcidid: 0000-0002-8552-043X surname: Sun fullname: Sun, Fengzhu
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SubjectTerms	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
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Title	DeepDeconUQ estimates malignant cell fraction prediction intervals in bulk RNA-seq tissue
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