The poor performance of TMM on microRNA-Seq

We published our third-party comparison of seven different normalization methods that were previously employed in microarray analysis, and TMM, a method developed by Robinson and Oshlack (2010) for RNA-Seq analysis, in the context of microRNA sequencing (miRNA-Seq). We used various evaluation metric...

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Bibliographic Details
Published in:RNA (Cambridge) 2013-06, Vol.19 (6), p.735-736
Main Authors: Garmire, Lana X, Subramaniam, Shankar
Format: Article
Language:English
Subjects:
Animals
Divergent Views
MicroRNAs - chemistry
MicroRNAs - genetics
Sequence Analysis, RNA - methods
Sequence Analysis, RNA - statistics & numerical data
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Summary:We published our third-party comparison of seven different normalization methods that were previously employed in microarray analysis, and TMM, a method developed by Robinson and Oshlack (2010) for RNA-Seq analysis, in the context of microRNA sequencing (miRNA-Seq). We used various evaluation metrics (MSE, KS statistics, ROC curves, linear regression, and differential expression test similarities) on two independent public miRNA-Seq profiling results that had qPCR results for validation. Based on the results that used relevant R packages at the time of publication, we found (Garmire and Subramaniam 2012) that quantile and lowess normalization worked the best on the two public data sets, whereas the normalization step documented in TMM, at the time of manuscript preparation, performed the worst among all methods compared.
ISSN:1355-8382
1469-9001
DOI:10.1261/rna.039271.113