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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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Pooling across cells to normalize single-cell RNA sequencing data with many zero counts.

Aaron T L Lun¹, Karsten Bach², John C Marioni^3,4,5

¹Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, CB2 0RE, Cambridge, UK. aaron.lun@cruk.cam.ac.uk.

|April 29, 2016

Summary

We developed a new method for normalizing single-cell RNA sequencing data by pooling cells. This approach accurately corrects for cell-specific biases, improving downstream analysis results.

Keywords:

Differential expression Normalization Single-cell RNA-seq

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Last Updated: Mar 22, 2026

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Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

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Published on: May 22, 2017

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
Normalization is essential to remove technical biases in scRNA-seq data.
Existing normalization methods struggle with the sparsity and noise inherent in scRNA-seq data.

Purpose of the Study:

To introduce a novel normalization strategy for scRNA-seq data.
To address the challenges posed by zero counts and cell-specific biases.
To improve the accuracy and reliability of downstream scRNA-seq analyses.

Main Methods:

A new pool-based normalization approach is presented.
Expression values are summed across cell pools for initial normalization.
Pool-based size factors are deconvolved to obtain cell-specific factors.

Main Results:

The deconvolution approach demonstrates superior performance in normalizing simulated scRNA-seq data.
Accurate correction of cell-specific biases was achieved.
The method shows comparable effectiveness on real-world scRNA-seq datasets.

Conclusions:

The proposed deconvolution method offers a robust solution for scRNA-seq data normalization.
This approach enhances the quality and interpretability of downstream analyses.
The method effectively mitigates biases in noisy, sparse single-cell datasets.