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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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Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

CrossNorm: a novel normalization strategy for microarray data in cancers.

Lixin Cheng¹, Leung-Yau Lo¹, Nelson L S Tang²

¹Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.

Scientific Reports

|January 7, 2016

Summary

This summary is machine-generated.

A new Cross Normalization (CrossNorm) strategy improves microarray data analysis by addressing global expression shifts common in cancer. This method enhances accuracy for transcript levels, benefiting cancer research and other high-throughput data with expression variations.

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Area of Science:

Bioinformatics
Genomics
Cancer Research

Background:

Microarray data analysis requires normalization to eliminate biases.
Current normalization methods often fail with global gene expression shifts prevalent in cancer.
Existing techniques like RMA and LOESS can introduce bias by assuming similar global expression patterns.

Purpose of the Study:

To develop a novel normalization strategy, Cross Normalization (CrossNorm), for microarray data with unbalanced transcript levels.
To overcome limitations of conventional methods in handling global expression variations, particularly in cancer samples.

Main Methods:

Proposed and developed the Cross Normalization (CrossNorm) strategy.
Applied CrossNorm in conjunction with conventional methods (e.g., RMA, LOESS) to analyze various datasets.
Utilized overall statistics of original signals within the CrossNorm strategy.

Main Results:

CrossNorm demonstrated significantly improved robustness and accuracy in estimating transcript levels.
Validated on diverse datasets: titration experiments, simulated data, spike-in data, and real-life cancer microarray data.
Showed effectiveness in datasets with non-negligible differences between sample groups.

Conclusions:

CrossNorm is a robust and accurate normalization strategy for microarray data with global expression variations.
The strategy has significant implications for cancer studies using microarray data.
CrossNorm is adaptable to other high-throughput data experiencing global expression variations between conditions.