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SERE: Single-parameter quality control and sample comparison for RNA-Seq

Stefan K Schulze, Rahul Kanwar, Meike Gölzenleuchter, Terry M Therneau* and Andreas S Beutler*

Author Affiliations

Departments of Oncology and Biostatistics, Mayo Clinic, Rochester, MN 55905, USA

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BMC Genomics 2012, 13:524  doi:10.1186/1471-2164-13-524

Published: 3 October 2012



Assessing the reliability of experimental replicates (or global alterations corresponding to different experimental conditions) is a critical step in analyzing RNA-Seq data. Pearson’s correlation coefficient r has been widely used in the RNA-Seq field even though its statistical characteristics may be poorly suited to the task.


Here we present a single-parameter test procedure for count data, the Simple Error Ratio Estimate (SERE), that can determine whether two RNA-Seq libraries are faithful replicates or globally different. Benchmarking shows that the interpretation of SERE is unambiguous regardless of the total read count or the range of expression differences among bins (exons or genes), a score of 1 indicating faithful replication (i.e., samples are affected only by Poisson variation of individual counts), a score of 0 indicating data duplication, and scores >1 corresponding to true global differences between RNA-Seq libraries. On the contrary the interpretation of Pearson’s r is generally ambiguous and highly dependent on sequencing depth and the range of expression levels inherent to the sample (difference between lowest and highest bin count). Cohen’s simple Kappa results are also ambiguous and are highly dependent on the choice of bins. For quantifying global sample differences SERE performs similarly to a measure based on the negative binomial distribution yet is simpler to compute.


SERE can therefore serve as a straightforward and reliable statistical procedure for the global assessment of pairs or large groups of RNA-Seq datasets by a single statistical parameter.

SERE; Simple Error Ratio Estimate; RNA-Seq; Pearson’s correlation coefficient; Replicates; Kappa; Poisson variation; Count data