Limits...
MetaDiff: differential isoform expression analysis using random-effects meta-regression.

Jia C, Guan W, Yang A, Xiao R, Tang WH, Moravec CS, Margulies KB, Cappola TP, Li M, Li C - BMC Bioinformatics (2015)

Bottom Line: Detection of differential isoform expression is challenging because of uncertainty in isoform expression estimation owing to ambiguous reads and variability in precision of the estimates across samples.It is desirable to have a method that can account for these issues and is flexible enough to allow adjustment for covariates.In contrast, several existing methods either fail to control false discovery rate or have reduced power in the presence of covariates or confounding variables.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. jiacheng@mail.med.upenn.edu.

ABSTRACT

Background: RNA sequencing (RNA-Seq) allows an unbiased survey of the entire transcriptome in a high-throughput manner. A major application of RNA-Seq is to detect differential isoform expression across experimental conditions, which is of great biological interest due to its direct relevance to protein function and disease pathogenesis. Detection of differential isoform expression is challenging because of uncertainty in isoform expression estimation owing to ambiguous reads and variability in precision of the estimates across samples. It is desirable to have a method that can account for these issues and is flexible enough to allow adjustment for covariates.

Results: In this paper, we present MetaDiff, a random-effects meta-regression model that naturally fits for the above purposes. Through extensive simulations and analysis of an RNA-Seq dataset on human heart failure, we show that the random-effects meta-regression approach is computationally fast, reliable, and can improve the power of differential expression analysis while controlling for false positives due to the effect of covariates or confounding variables. In contrast, several existing methods either fail to control false discovery rate or have reduced power in the presence of covariates or confounding variables. The source code, compiled JAR package and documentation of MetaDiff are freely available at https://github.com/jiach/MetaDiff.

Conclusion: Our results indicate that random-effects meta-regression offers a flexible framework for differential expression analysis of isoforms, particularly when gene expression is influenced by other variables.

No MeSH data available.


Related in: MedlinePlus

Analogy between meta-regression and isoform differential expression analysis in RNA-Seq
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4489045&req=5

Fig1: Analogy between meta-regression and isoform differential expression analysis in RNA-Seq

Mentions: In this paper, we present MetaDiff, a regression framework based on a random-effects meta-regression model that can be considered as a frequentist version of MMDIFF [16]. The original goal of random-effects meta-regression is to synthesize results from multiple studies while accounting for varying standard errors of the effect estimates by explicitly allowing for different sources of variability: within- and between-study variation. Its mathematical model matches perfectly with the analysis of DE isoform in that within-study variation represents variable precision in isoform expression estimation and between-study variation represents variation in isoform expression levels across samples (Fig. 1). This analogy motivated us to explore random-effects meta-regression as a means for identification of DE isoforms.Fig. 1


MetaDiff: differential isoform expression analysis using random-effects meta-regression.

Jia C, Guan W, Yang A, Xiao R, Tang WH, Moravec CS, Margulies KB, Cappola TP, Li M, Li C - BMC Bioinformatics (2015)

Analogy between meta-regression and isoform differential expression analysis in RNA-Seq
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4489045&req=5

Fig1: Analogy between meta-regression and isoform differential expression analysis in RNA-Seq
Mentions: In this paper, we present MetaDiff, a regression framework based on a random-effects meta-regression model that can be considered as a frequentist version of MMDIFF [16]. The original goal of random-effects meta-regression is to synthesize results from multiple studies while accounting for varying standard errors of the effect estimates by explicitly allowing for different sources of variability: within- and between-study variation. Its mathematical model matches perfectly with the analysis of DE isoform in that within-study variation represents variable precision in isoform expression estimation and between-study variation represents variation in isoform expression levels across samples (Fig. 1). This analogy motivated us to explore random-effects meta-regression as a means for identification of DE isoforms.Fig. 1

Bottom Line: Detection of differential isoform expression is challenging because of uncertainty in isoform expression estimation owing to ambiguous reads and variability in precision of the estimates across samples.It is desirable to have a method that can account for these issues and is flexible enough to allow adjustment for covariates.In contrast, several existing methods either fail to control false discovery rate or have reduced power in the presence of covariates or confounding variables.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA. jiacheng@mail.med.upenn.edu.

ABSTRACT

Background: RNA sequencing (RNA-Seq) allows an unbiased survey of the entire transcriptome in a high-throughput manner. A major application of RNA-Seq is to detect differential isoform expression across experimental conditions, which is of great biological interest due to its direct relevance to protein function and disease pathogenesis. Detection of differential isoform expression is challenging because of uncertainty in isoform expression estimation owing to ambiguous reads and variability in precision of the estimates across samples. It is desirable to have a method that can account for these issues and is flexible enough to allow adjustment for covariates.

Results: In this paper, we present MetaDiff, a random-effects meta-regression model that naturally fits for the above purposes. Through extensive simulations and analysis of an RNA-Seq dataset on human heart failure, we show that the random-effects meta-regression approach is computationally fast, reliable, and can improve the power of differential expression analysis while controlling for false positives due to the effect of covariates or confounding variables. In contrast, several existing methods either fail to control false discovery rate or have reduced power in the presence of covariates or confounding variables. The source code, compiled JAR package and documentation of MetaDiff are freely available at https://github.com/jiach/MetaDiff.

Conclusion: Our results indicate that random-effects meta-regression offers a flexible framework for differential expression analysis of isoforms, particularly when gene expression is influenced by other variables.

No MeSH data available.


Related in: MedlinePlus