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

BMC Genomics

Methodology Article

Rahul Kanwar¹  Meike Gölzenleuchter¹  Andreas S Beutler¹  Stefan K Schulze¹  Terry M Therneau¹ 

[1] Departments of Oncology and Biostatistics, Mayo Clinic, 55905, Rochester, MN, USA;

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

DOI  :  10.1186/1471-2164-13-524

 received in 2012-07-11, accepted in 2012-09-18,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundAssessing 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.ResultsHere 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.ConclusionsSERE 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.

【 授权许可】

Unknown   
© Schulze et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.