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Analyzing Microarray Data

Authors: Hung, Jui-Hung; Weng, Zhiping;

Analyzing Microarray Data

Abstract

Because there is no widely used software for analyzing RNA-seq data that has a graphical user interface, this protocol provides an example of analyzing microarray data using Babelomics. This analysis entails performing quantile normalization and then detecting differentially expressed genes associated with the transgenesis of a human oncogene c-Myc in mice. Finally, hierarchical clustering is performed on the differentially expressed genes using the Cluster program, and the results are visualized using TreeView.

Country
United States
Related Organizations
Keywords

Mice, Bioinformatics, Sequence Analysis, RNA, Gene Expression Profiling, Computational Biology, Animals, Humans, Microarray Analysis

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    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Top 10%
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