Dataset Information

PROPER: comprehensive power evaluation for differential expression using RNA-seq.

ABSTRACT:

Motivation

RNA-seq has become a routine technique in differential expression (DE) identification. Scientists face a number of experimental design decisions, including the sample size. The power for detecting differential expression is affected by several factors, including the fraction of DE genes, distribution of the magnitude of DE, distribution of gene expression level, sequencing coverage and the choice of type I error control. The complexity and flexibility of RNA-seq experiments, the high-throughput nature of transcriptome-wide expression measurements and the unique characteristics of RNA-seq data make the power assessment particularly challenging.

Results

We propose prospective power assessment instead of a direct sample size calculation by making assumptions on all of these factors. Our power assessment tool includes two components: (i) a semi-parametric simulation that generates data based on actual RNA-seq experiments with flexible choices on baseline expressions, biological variations and patterns of DE; and (ii) a power assessment component that provides a comprehensive view of power. We introduce the concepts of stratified power and false discovery cost, and demonstrate the usefulness of our method in experimental design (such as sample size and sequencing depth), as well as analysis plan (gene filtering).

Availability

The proposed method is implemented in a freely available R software package PROPER.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Wu H

PROVIDER: S-EPMC4287952 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:RNA-seq is a high-throughput sequencing technology widely used for gene transcript discovery and quantification under different biological or biomedical conditions. A fundamental research question in most RNA-seq experiments is the identification of differentially expressed genes among experimental conditions or sample groups. Numerous statistical methods for RNA-seq differential analysis have been proposed since the emergence of the RNA-seq assay. To evaluate popular differential analysis methods used in the open source R and Bioconductor packages, we conducted multiple simulation studies to compare the performance of eight RNA-seq differential analysis methods used in RNA-seq data analysis (edgeR, DESeq, DESeq2, baySeq, EBSeq, NOISeq, SAMSeq, Voom). The comparisons were across different scenarios with either equal or unequal library sizes, different distribution assumptions and sample sizes. We measured performance using false discovery rate (FDR) control, power, and stability. No significant differences were observed for FDR control, power, or stability across methods, whether with equal or unequal library sizes. For RNA-seq count data with negative binomial distribution, when sample size is 3 in each group, EBSeq performed better than the other methods as indicated by FDR control, power, and stability. When sample sizes increase to 6 or 12 in each group, DESeq2 performed slightly better than other methods. All methods have improved performance when sample size increases to 12 in each group except DESeq. For RNA-seq count data with log-normal distribution, both DESeq and DESeq2 methods performed better than other methods in terms of FDR control, power, and stability across all sample sizes. Real RNA-seq experimental data were also used to compare the total number of discoveries and stability of discoveries for each method. For RNA-seq data analysis, the EBSeq method is recommended for studies with sample size as small as 3 in each group, and the DESeq2 method is recommended for sample size of 6 or higher in each group when the data follow the negative binomial distribution. Both DESeq and DESeq2 methods are recommended when the data follow the log-normal distribution.