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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

Simulation setup. Simulation setup when isoform expression is influenced by a variable either as a covariate (Scenario II) or as a confounder (Scenario III)
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Fig2: Simulation setup. Simulation setup when isoform expression is influenced by a variable either as a covariate (Scenario II) or as a confounder (Scenario III)

Mentions: Next, we considered a situation in which some gene expression levels were influenced by a covariate (e.g., age) (Scenario II). The distribution of the covariate was uniform from 18 to 60 in both cases and controls. Similar to Scenario I, 30 % of the transcripts were designated to be DE and 70 % were non-DE. We further allowed 10 % of the transcripts to be affected by the covariate, half with differential expression between the cases and controls and half without differential expression; these transcripts had 1.35 fold increased expression for every one standard deviation increase in the covariate, which is equivalent to 2.5 % increased expression for every one unit increase of the covariate. Detailed simulation setup is shown in Fig. 2.Fig. 2


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)

Simulation setup. Simulation setup when isoform expression is influenced by a variable either as a covariate (Scenario II) or as a confounder (Scenario III)
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Simulation setup. Simulation setup when isoform expression is influenced by a variable either as a covariate (Scenario II) or as a confounder (Scenario III)
Mentions: Next, we considered a situation in which some gene expression levels were influenced by a covariate (e.g., age) (Scenario II). The distribution of the covariate was uniform from 18 to 60 in both cases and controls. Similar to Scenario I, 30 % of the transcripts were designated to be DE and 70 % were non-DE. We further allowed 10 % of the transcripts to be affected by the covariate, half with differential expression between the cases and controls and half without differential expression; these transcripts had 1.35 fold increased expression for every one standard deviation increase in the covariate, which is equivalent to 2.5 % increased expression for every one unit increase of the covariate. Detailed simulation setup is shown in Fig. 2.Fig. 2

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