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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.


Related in: MedlinePlus

Noise properties of RNA-seq technical replicate data. In every panel, the mean vs. the Fano factor for individual genes is plotted. Data were taken from [19]. Black circles correspond to individual genes, and the red line corresponds to a linear regression of the mean vs. the Fano factor for the indicated number of genes. Behavior of the (A) first 15,000 genes; (B) first 19,000 genes; or (C) first 19,600 genes as ranked by increasing mean.
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Figure 3: Noise properties of RNA-seq technical replicate data. In every panel, the mean vs. the Fano factor for individual genes is plotted. Data were taken from [19]. Black circles correspond to individual genes, and the red line corresponds to a linear regression of the mean vs. the Fano factor for the indicated number of genes. Behavior of the (A) first 15,000 genes; (B) first 19,000 genes; or (C) first 19,600 genes as ranked by increasing mean.


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)

Noise properties of RNA-seq technical replicate data. In every panel, the mean vs. the Fano factor for individual genes is plotted. Data were taken from [19]. Black circles correspond to individual genes, and the red line corresponds to a linear regression of the mean vs. the Fano factor for the indicated number of genes. Behavior of the (A) first 15,000 genes; (B) first 19,000 genes; or (C) first 19,600 genes as ranked by increasing mean.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Noise properties of RNA-seq technical replicate data. In every panel, the mean vs. the Fano factor for individual genes is plotted. Data were taken from [19]. Black circles correspond to individual genes, and the red line corresponds to a linear regression of the mean vs. the Fano factor for the indicated number of genes. Behavior of the (A) first 15,000 genes; (B) first 19,000 genes; or (C) first 19,600 genes as ranked by increasing mean.
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.


Related in: MedlinePlus