The fine detail provided by sequencing-based transcriptome surveys
suggests that RNA-seq is likely to become the platform of choice
for interrogating steady state RNA. In order to discover biologically
important changes in expression, we show that normalization continues
to be an essential step in the analysis. We outline a simple and
effective method for performing normalization and show dramatically
improved results for inferring differential expression in simulated
and publicly available data sets.
%0 Journal Article
%1 Robinson2010scalingnormalizationmethod
%A Robinson, Mark D
%A Oshlack, Alicia
%D 2010
%J Genome Biol
%K Base Computer Expression Gene Library; Models, Profiling, RNA, Sequence, Simulation; Statistical; genetics genetics; methods;
%N 3
%P R25
%R 10.1186/gb-2010-11-3-r25
%T A scaling normalization method for differential expression analysis
of RNA-seq data.
%U http://dx.doi.org/10.1186/gb-2010-11-3-r25
%V 11
%X The fine detail provided by sequencing-based transcriptome surveys
suggests that RNA-seq is likely to become the platform of choice
for interrogating steady state RNA. In order to discover biologically
important changes in expression, we show that normalization continues
to be an essential step in the analysis. We outline a simple and
effective method for performing normalization and show dramatically
improved results for inferring differential expression in simulated
and publicly available data sets.
@article{Robinson2010scalingnormalizationmethod,
abstract = {The fine detail provided by sequencing-based transcriptome surveys
suggests that RNA-seq is likely to become the platform of choice
for interrogating steady state RNA. In order to discover biologically
important changes in expression, we show that normalization continues
to be an essential step in the analysis. We outline a simple and
effective method for performing normalization and show dramatically
improved results for inferring differential expression in simulated
and publicly available data sets.},
added-at = {2014-05-13T15:48:44.000+0200},
author = {Robinson, Mark D and Oshlack, Alicia},
biburl = {https://www.bibsonomy.org/bibtex/2eff41f3b4eb1a507d71362d251753c77/gwotto},
doi = {10.1186/gb-2010-11-3-r25},
file = {:Robinson2010scalingnormalizationmethod.pdf:PDF},
institution = {Bioinformatics Division{\,} Walter and Eliza Hall Institute{\,} 1G
Royal Parade{\,} Parkville{\,} Australia. mrobinson@wehi.edu.au},
interhash = {3c778733a8ffe2b3e6c4df6823484947},
intrahash = {eff41f3b4eb1a507d71362d251753c77},
journal = {Genome Biol},
keywords = {Base Computer Expression Gene Library; Models, Profiling, RNA, Sequence, Simulation; Statistical; genetics genetics; methods;},
language = {eng},
medline-pst = {ppublish},
number = 3,
owner = {gotto},
pages = {R25},
pii = {gb-2010-11-3-r25},
pmid = {20196867},
timestamp = {2014-05-13T15:48:44.000+0200},
title = {A scaling normalization method for differential expression analysis
of RNA-seq data.},
url = {http://dx.doi.org/10.1186/gb-2010-11-3-r25},
volume = 11,
year = 2010
}