A Comparison of Methods for RNA-Seq Differential Expression Analysis and a New Empirical Bayes Approach.
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.
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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. |
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Mentions: To evaluate how R-EBSeq performs, we generated a test data set according to the underlying empirical Bayes model, imposed differential expression on a subset of transcripts from this data set and, then, calculated the performance of R-EBSeq in terms of true positive and false positive identifications (see Methods). Such plots of the false positive rate vs. the true positive rate are called receiver operator characteristic (ROC) curves. An ROC curve along the x = y line implies a very poor algorithm that performs no better than random choice, whereas an ROC curve that peaks high above the x = y line, at low x values, implies a very good algorithm. We investigated how three characteristics of a transcript affect the ROC curves: the difference of means between two conditions (Figure 4(A)), the variance of the transcript expression level (Figure 4(B)), and the number of replicates, M, used as input to the software (Figure 4(C)). In general, we see that R-EBSeq is capable of very good behavior in terms of the ROC curves. As expected, as we increase the difference of means between two conditions and/or decrease the variance, the ability of R-EBSeq to identify truly differentially expressed genes improves. Increasing the number of replicates, M, also improves the performance of R-EBSeq, likely because R-EBSeq is able to get a better estimate of a transcriptâ€™s variance. |
View Article: PubMed Central - PubMed
Affiliation: Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA ; E-Mail: wesserg@gmail.com.
No MeSH data available.