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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...

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Deconvolution of Sparse-count RNA Sequencing Data for Tumor Cells Using Embedded Negative Binomial Distributions.

Matthew D Montierth, Hao Yan, Liyang Xie

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|December 15, 2025

Summary

This summary is machine-generated.

We developed DeMixNB, a new computational method to accurately estimate tumor cell proportions in small RNA sequencing data. This tool helps uncover novel cancer biology, particularly in challenging sparse-count datasets like microRNA-seq and spatial transcriptomics.

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Micromanipulation of Circulating Tumor Cells for Downstream Molecular Analysis and Metastatic Potential Assessment

Micromanipulation of Circulating Tumor Cells for Downstream Molecular Analysis and Metastatic Potential Assessment

Published on: May 14, 2019

Area of Science:

Computational Biology
Genomics
Cancer Research

Background:

Estimating tumor-specific transcript proportions from mixed samples is crucial for understanding cancer biology.
Existing methods struggle with accuracy in sparse-count data like microRNA-seq and spatial transcriptomics.
Analytical challenges in deconvolution of mixed small RNA data require novel solutions.

Purpose of the Study:

To develop and validate a robust deconvolution model for estimating tumor cell transcript proportions.
To address the limitations of current methods in sparse-count RNA sequencing data.
To provide a tool for investigating cancer RNomes and tumor cell plasticity.

Main Methods:

Developed DeMixNB, a semi-reference-based deconvolution model.
The model assumes a sum of negative binomial distributions to handle count data.
Generated a mixed small RNA benchmark dataset to demonstrate analytical challenges and validate the method.

Main Results:

DeMixNB demonstrated improved accuracy in estimating tumor-specific transcript proportions.
Applications to breast cancer microRNA-seq data revealed clinical insights.
Analysis of lung cancer spatial transcriptomics data provided mechanistic insights into tumor cell plasticity.

Conclusions:

DeMixNB is a valuable tool for deconvolution of sparse-count RNA sequencing data.
The method enhances the investigation of cancer RNomes and tumor cell plasticity.
DeMixNB offers significant utility for uncovering novel cancer biology from mixed samples.