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DeepDeconUQ估计了大量RNA-seq组织中的恶性细胞分数预测间隔

Jiawei Huang¹, Yuxuan Du^1,2, Kevin R Kelly³

¹Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America.

PLoS computational biology

|June 4, 2025

概括

DeepDeconUQ使用大量RNA-seq数据量化恶性细胞分数估计的不确定性. 这种深度学习模型提供了可靠的预测间隔,改善了癌症诊断和研究准确性.

科学领域:

计算生物学计算生物学
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

准确的恶性细胞分数估计对于癌症诊断,预后和治疗至关重要.
目前的方法往往缺乏不确定性量化,限制了它们的临床和研究实用性.

研究的目的:

介绍DeepDeconUQ,一个深度神经网络模型,用于从大量RNA-seq数据中估计恶性细胞部分的预测间隔.
将不确定性量化整合到癌细胞分数预测中.

主要方法:

DeepDeconUQ利用单细胞RNA测序 (scRNA-seq) 数据和符合规范的定量回归.
一个量子回归神经网络建立了预测间隔的界限,随后是统计有效性和区分的校准步骤.

主要成果:

在模拟和真实癌症数据集上,DeepDeconUQ在覆盖准确性和间隔紧密性方面优于现有方法.
该模型证明了对基因表达干扰的稳定性.

结论:

DeepDeconUQ提供了一个强大的方法来量化恶性细胞分数估计的不确定性.
这种方法提高了癌症细胞分数预测的可靠性,用于临床和研究应用.

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相关概念视频

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...