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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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Related Experiment Video

Updated: Dec 2, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Correcting for experiment-specific variability in expression compendia can remove underlying signals.

Alexandra J Lee^1,2, YoSon Park², Georgia Doing³

¹Genomics and Computational Biology Graduate Program, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA.

|November 3, 2020

Summary

This summary is machine-generated.

Combining gene expression data can reveal biological patterns, but technical variability (batch effects) can interfere. Our study shows that correcting for batch effects is beneficial for a few experiments, but detrimental when many are combined, hindering pattern discovery.

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Last Updated: Dec 2, 2025

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Published on: August 3, 2018

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

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Published on: March 7, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Millions of gene expression samples have been generated over two decades.
Combining these samples into compendia allows for collective analysis to uncover novel biological patterns.
Technical variability, or batch effects, arising from different experimental conditions can obscure true biological signals.

Purpose of the Study:

To investigate the impact of technical variability on the ability to detect biological patterns in large gene expression compendia.
To determine how the extent of technical variability affects the efficacy of data correction methods.

Main Methods:

Developed a generative multi-layer neural network to simulate gene expression compendia.
Simulated compendia using large-scale microbial and human datasets.
Introduced varying levels of technical variability to assess its impact on signal detection and correction.

Main Results:

A small number of added variability sources obscured the underlying biological signal, which was rescued by statistical correction.
As sources of variability increased, the original signal became detectable even without correction.
Statistical correction methods reduced the power to detect the underlying signal when numerous variability sources were present.

Conclusions:

Correcting for experiment-specific noise is advisable when combining a modest number of gene expression experiments.
When integrating a large number of experiments, statistical correction can impede the extraction of underlying biological patterns.