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A method to assess significant differences in RNA expression among specific gene groups

View ORCID ProfileMingze He, View ORCID ProfilePeng Liu, View ORCID ProfileCarolyn J. Lawrence-Dill
doi: https://doi.org/10.1101/136143
Mingze He
1Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA, 50011
2Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa, USA 50011
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  • ORCID record for Mingze He
Peng Liu
1Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA, 50011
3Department of Statistics, Iowa State University, Ames, Iowa, USA 50011
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Carolyn J. Lawrence-Dill
1Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa, USA, 50011
2Department of Genetics, Development and Cell Biology, Iowa State University, Ames, Iowa, USA 50011
4Department of Agronomy, Iowa State University, Ames, Iowa, USA 50011
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  • For correspondence: triffid@iastate.edu
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Abstract

Most expression studies measure transcription rates across multiple conditions followed by clustering and functional enrichment. This enables discovery of shared function for differentially expressed genes, but is not useful for determining whether pre-defined groups of genes share or diverge in their expression patterns. Here we present a simple data transformation method that allows Gaussian parametric statistical analysis of expression for groups of genes, thus enabling a biologically relevant hypothesis-driven approach to gene expression analysis.

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Posted May 12, 2017.
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A method to assess significant differences in RNA expression among specific gene groups
Mingze He, Peng Liu, Carolyn J. Lawrence-Dill
bioRxiv 136143; doi: https://doi.org/10.1101/136143
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A method to assess significant differences in RNA expression among specific gene groups
Mingze He, Peng Liu, Carolyn J. Lawrence-Dill
bioRxiv 136143; doi: https://doi.org/10.1101/136143

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