Evaluation of normalization methods in mammalian microRNA-Seq data

RNAResearchers in the Department of Bioengineering, UCSD evaluated seven commonly used normalization methods:

Global normalization
Lowess normalization
Trimmed Mean Method (TMM)
Quantile normalization
Scaling normalization
Variance stabilization
Invariant method

They assessed these methods on two individual experimental data sets with the empirical statistical metrics of mean square error (MSE) and Kolmogorov-Smirnov (K-S) statistic.

The results consistently show that Lowess normalization and quantile normalization perform the best, whereas TMM, a method applied to the RNA-Sequencing normalization, performs the worst.

The poor performance of TMM normalization is further evidenced by abnormal results from the test of differential expression (DE) of microRNA-Seq data. Comparing with the models used for DE, the choice of normalization method is the primary factor that affects the results of DE. In summary, Lowess normalization and quantile normalization are recommended for normalizing microRNA-Seq data, whereas the TMM method should be used with caution.

  • Garmire LX, Subramaniam S. (2012)v Evaluation of normalization methods in mammalian microRNA-Seq data. RNA [Epub ahead of print]. [abstract]

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