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Related Articles Statistical Design and Analysis of RNA-Seq Data.
Genetics. 2010 May 3;
Authors: Auer PL, Doerge RW
Next-generation sequencing technologies are quickly becoming the preferred approach for characterizing and quantifying entire genomes. Even though data produced from these technologies are proving to be the most informative of any thus far, very little attention has been paid to fundamental design aspects of data collection and analysis, namely sampling, randomization, replication, and blocking. We discuss these concepts in an RNA-Sequencing framework that makes limited use of multiplexing. Using simulations we demonstrate the benefits of collecting replicated RNA-Sequencing data according to well known statistical designs that partition the sources of biological and technical variation. Examples of these designs and their corresponding models are presented with the goal of testing differential expression.
PMID: 20439781 [PubMed - as supplied by publisher]
More...
Related Articles Statistical Design and Analysis of RNA-Seq Data.
Genetics. 2010 May 3;
Authors: Auer PL, Doerge RW
Next-generation sequencing technologies are quickly becoming the preferred approach for characterizing and quantifying entire genomes. Even though data produced from these technologies are proving to be the most informative of any thus far, very little attention has been paid to fundamental design aspects of data collection and analysis, namely sampling, randomization, replication, and blocking. We discuss these concepts in an RNA-Sequencing framework that makes limited use of multiplexing. Using simulations we demonstrate the benefits of collecting replicated RNA-Sequencing data according to well known statistical designs that partition the sources of biological and technical variation. Examples of these designs and their corresponding models are presented with the goal of testing differential expression.
PMID: 20439781 [PubMed - as supplied by publisher]
More...