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

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.