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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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Related Experiment Video

Updated: Sep 20, 2025

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

deMULTIplex2: robust sample demultiplexing for scRNA-seq.

Qin Zhu¹, Daniel N Conrad², Zev J Gartner^3,4,5

¹Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, 94158, USA. qin.zhu@ucsf.edu.

|January 30, 2024

Summary

This summary is machine-generated.

deMULTIplex2 accurately identifies cell origins in pooled single-cell RNA sequencing by modeling barcode cross-contamination. This new algorithm improves throughput and reduces batch effects, especially for complex datasets.

Keywords:

Demultiplex Expectation–maximization Generalized linear models Sample multiplexing scRNA-seq

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Published on: March 12, 2021

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Published on: March 23, 2022

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Sample multiplexing enhances single-cell RNA sequencing (scRNA-seq) by enabling pooled analysis, increasing throughput, and mitigating batch effects.
A critical challenge in multiplexed scRNA-seq is accurately linking sample-specific and cell-specific barcodes for post-sequencing demultiplexing.
Current demultiplexing tools often falter in real-world scenarios complicated by barcode cross-contamination.

Purpose of the Study:

To develop a robust algorithm for demultiplexing single-cell RNA sequencing data that addresses barcode cross-contamination.
To improve the accuracy and reliability of sample identification in pooled scRNA-seq experiments.

Main Methods:

Developed deMULTIplex2, an algorithm based on a mechanistic model of barcode cross-contamination.
Employed generalized linear models and expectation-maximization for probabilistic cell sample identity determination.
Benchmarked deMULTIplex2 performance against existing methods across diverse experimental conditions.

Main Results:

deMULTIplex2 demonstrates superior performance in demultiplexing single-cell RNA sequencing data.
The algorithm shows particular effectiveness on large, noisy, or unbalanced datasets.
Successfully addresses challenges posed by barcode cross-contamination, a common issue in multiplexing.

Conclusions:

deMULTIplex2 provides a significant advancement for demultiplexing multiplexed single-cell RNA sequencing data.
The algorithm enhances the reliability of sample identification, crucial for accurate pooled analysis.
Offers a robust solution for researchers dealing with complex and challenging scRNA-seq datasets.