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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...

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Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

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Published on: April 11, 2016

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

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

Summary

DeepDeconUQ quantifies uncertainty in malignant cell fraction estimation using bulk RNA-seq data. This deep learning model provides reliable prediction intervals, improving cancer diagnosis and research accuracy.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Accurate malignant cell fraction estimation is crucial for cancer diagnosis, prognosis, and treatment.
Current methods often lack uncertainty quantification, limiting their clinical and research utility.

Purpose of the Study:

To introduce DeepDeconUQ, a deep neural network model for estimating prediction intervals of malignant cell fractions from bulk RNA-seq data.
To integrate uncertainty quantification into cancer cell fraction predictions.

Main Methods:

DeepDeconUQ utilizes single-cell RNA sequencing (scRNA-seq) data and conformalized quantile regression.
A quantile regression neural network establishes prediction interval bounds, followed by a calibration step for statistical validity and discrimination.

Main Results:

DeepDeconUQ outperforms existing methods in coverage accuracy and interval tightness on simulated and real cancer datasets.
The model demonstrates robustness against gene expression perturbations.

Conclusions:

DeepDeconUQ offers a robust approach for quantifying uncertainty in malignant cell fraction estimation.
This method enhances the reliability of cancer cell fraction predictions for clinical and research applications.