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A Comparison of Methods for RNA-Seq Differential Expression Analysis and a New Empirical Bayes Approach.

Wesolowski S, Birtwistle MR, Rempala GA - Biosensors (Basel) (2013)

Bottom Line: Our analysis indicates that DESeq identifies the first half of the differentially expressed transcripts well, but then is outperformed by Cuffdiff and R-EBSeq.Cuffdiff and R-EBSeq are the two top performers.Thus, R-EBSeq offers good performance, while allowing flexible and rigorous comparison of multiple biological conditions.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA ; E-Mail: wesserg@gmail.com.

ABSTRACT
Transcriptome-based biosensors are expected to have a large impact on the future of biotechnology. However, a central aspect of transcriptomics is differential expression analysis, where, currently, deep RNA sequencing (RNA-seq) has the potential to replace the microarray as the standard assay for RNA quantification. Our contributions here to RNA-seq differential expression analysis are two-fold. First, given the high cost of an RNA-seq run, biological replicates are rare, and therefore, information sharing across genes to obtain variance estimates is crucial. To handle such information sharing in a rigorous manner, we propose an hierarchical, empirical Bayes approach (R-EBSeq) that combines the Cufflinks model for generating relative transcript abundance measurements, known as FPKM (fragments per kilobase of transcript length per million mapped reads) with the EBArrays framework, which was previously developed for empirical Bayes analysis of microarray data. A desirable feature of R-EBSeq is easy-to-implement analysis of more than pairwise comparisons, as we illustrate with experimental data. Secondly, we develop the standard RNA-seq test data set, on the level of reads, where 79 transcripts are artificially differentially expressed and, therefore, explicitly known. This test data set allows us to compare the performance, in terms of the true discovery rate, of R-EBSeq to three other widely used RNAseq data analysis packages: Cuffdiff, DEseq and BaySeq. Our analysis indicates that DESeq identifies the first half of the differentially expressed transcripts well, but then is outperformed by Cuffdiff and R-EBSeq. Cuffdiff and R-EBSeq are the two top performers. Thus, R-EBSeq offers good performance, while allowing flexible and rigorous comparison of multiple biological conditions.

No MeSH data available.


The RNA-seq pipeline. This schematic illustrates the process of going from cells to RNA-seq data, with potential sources of bias and variability noted along the way. See the Introduction for details.
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Figure 1: The RNA-seq pipeline. This schematic illustrates the process of going from cells to RNA-seq data, with potential sources of bias and variability noted along the way. See the Introduction for details.

Mentions: One application of next generation sequencing is transcriptomics, where an entire set of mRNAs is sequenced [6,7]. This application is known as RNA-seq, and the process, from cells to data and differential expression analysis, may be thought of as a pipeline, as shown in Figure 1. Although a wide variety of next generation sequencing platforms exist, they are generally based on slight variations of this core pipeline.


A Comparison of Methods for RNA-Seq Differential Expression Analysis and a New Empirical Bayes Approach.

Wesolowski S, Birtwistle MR, Rempala GA - Biosensors (Basel) (2013)

The RNA-seq pipeline. This schematic illustrates the process of going from cells to RNA-seq data, with potential sources of bias and variability noted along the way. See the Introduction for details.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263583&req=5

Figure 1: The RNA-seq pipeline. This schematic illustrates the process of going from cells to RNA-seq data, with potential sources of bias and variability noted along the way. See the Introduction for details.
Mentions: One application of next generation sequencing is transcriptomics, where an entire set of mRNAs is sequenced [6,7]. This application is known as RNA-seq, and the process, from cells to data and differential expression analysis, may be thought of as a pipeline, as shown in Figure 1. Although a wide variety of next generation sequencing platforms exist, they are generally based on slight variations of this core pipeline.

Bottom Line: Our analysis indicates that DESeq identifies the first half of the differentially expressed transcripts well, but then is outperformed by Cuffdiff and R-EBSeq.Cuffdiff and R-EBSeq are the two top performers.Thus, R-EBSeq offers good performance, while allowing flexible and rigorous comparison of multiple biological conditions.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA ; E-Mail: wesserg@gmail.com.

ABSTRACT
Transcriptome-based biosensors are expected to have a large impact on the future of biotechnology. However, a central aspect of transcriptomics is differential expression analysis, where, currently, deep RNA sequencing (RNA-seq) has the potential to replace the microarray as the standard assay for RNA quantification. Our contributions here to RNA-seq differential expression analysis are two-fold. First, given the high cost of an RNA-seq run, biological replicates are rare, and therefore, information sharing across genes to obtain variance estimates is crucial. To handle such information sharing in a rigorous manner, we propose an hierarchical, empirical Bayes approach (R-EBSeq) that combines the Cufflinks model for generating relative transcript abundance measurements, known as FPKM (fragments per kilobase of transcript length per million mapped reads) with the EBArrays framework, which was previously developed for empirical Bayes analysis of microarray data. A desirable feature of R-EBSeq is easy-to-implement analysis of more than pairwise comparisons, as we illustrate with experimental data. Secondly, we develop the standard RNA-seq test data set, on the level of reads, where 79 transcripts are artificially differentially expressed and, therefore, explicitly known. This test data set allows us to compare the performance, in terms of the true discovery rate, of R-EBSeq to three other widely used RNAseq data analysis packages: Cuffdiff, DEseq and BaySeq. Our analysis indicates that DESeq identifies the first half of the differentially expressed transcripts well, but then is outperformed by Cuffdiff and R-EBSeq. Cuffdiff and R-EBSeq are the two top performers. Thus, R-EBSeq offers good performance, while allowing flexible and rigorous comparison of multiple biological conditions.

No MeSH data available.