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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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Updated: Nov 1, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Normalization by distributional resampling of high throughput single-cell RNA-sequencing data.

Jared Brown¹, Zijian Ni¹, Chitrasen Mohanty²

¹Department of Statistics, University of Wisconsin Madison, Madison, WI 53706, USA.

Bioinformatics (Oxford, England)

|June 19, 2021

Summary

Dino is a new normalization method for single-cell RNA sequencing data. It improves downstream analysis by normalizing the entire gene expression distribution, outperforming existing methods.

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Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Normalization is crucial for single-cell RNA sequencing (scRNA-seq) to remove technical artifacts.
Existing normalization methods often adjust for library size (LS), leading to issues with gene expression distribution properties and increased false discoveries.
High proportions of zero counts in scRNA-seq datasets exacerbate these normalization challenges.

Purpose of the Study:

To introduce Dino, a novel normalization method for scRNA-seq data.
To address limitations of existing library size-based normalization techniques.
To improve the accuracy and power of downstream analyses in scRNA-seq.

Main Methods:

Dino employs a flexible negative-binomial mixture model for gene expression normalization.
The method normalizes the entire gene expression distribution, not just average expression.
The Dino R package is available on GitHub and Zenodo.

Main Results:

Dino demonstrates robustness to shallow sequencing, sample heterogeneity, and varying zero proportions.
Simulated and case study datasets show improved performance of Dino in downstream analyses.
Normalization using Dino leads to reduced false discoveries and increased analytical power.

Conclusions:

Dino offers a more robust and effective approach to normalizing scRNA-seq data.
The method enhances the reliability of downstream analyses by accounting for the full gene expression distribution.
Dino provides a valuable tool for scRNA-seq data analysis, available as an R package.