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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Mar 17, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

NanoStringDiff: a novel statistical method for differential expression analysis based on NanoString nCounter data.

Hong Wang¹, Craig Horbinski², Hao Wu³

¹Department of Statistics, University of Kentucky, Lexington, KY 40536, USA.

Nucleic Acids Research

|July 30, 2016

Summary

This summary is machine-generated.

We developed NanoStringDiff, a new method for analyzing NanoString nCounter data to detect differential gene expression. This approach accurately handles count data and improves normalization for more reliable results in molecular studies.

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Area of Science:

Genomics
Bioinformatics
Molecular Biology

Background:

NanoString nCounter technology is valuable for mRNA/miRNA differential expression (DE) studies.
Existing DE analysis methods are unsuitable for NanoString's digital count data.
Current normalization methods lack specificity for NanoString data.

Purpose of the Study:

To develop a novel method for differential expression analysis of NanoString nCounter data.
To address limitations of existing methods regarding count data and normalization.
To provide a robust tool for researchers using NanoString technology.

Main Methods:

Utilized a generalized linear model from the negative binomial family for count data.
Incorporated data normalization within the model using parameters from controls.
Employed an empirical Bayes shrinkage approach for dispersion parameter estimation.
Used a likelihood ratio test for identifying differentially expressed genes.

Main Results:

The proposed NanoStringDiff method accurately models NanoString count data.
Integrated normalization improves data quality and reliability.
Simulations and real data confirmed superior performance compared to existing methods.

Conclusions:

NanoStringDiff offers an improved approach for differential expression analysis on NanoString nCounter data.
The method is suitable for multifactor experimental designs.
This advancement enhances the utility of NanoString technology in molecular research.