Bayesian inference with historical data-based informative priors improves detection of differentially expressed genes

Li, Ben; Sun, Zhaonan; He, Qing-Yu; Zhu, Yu; Qin, Zhaohui

doi:10.1093/bioinformatics/btv631

Cited by 11 publications

(10 citation statements)

References 29 publications

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“…For example, in pediatric studies Bayesian methods are typically used to explicitly borrow information from other adult trials or previous pediatric studies . See also Viele et al, Ibrahim et al, Takeda et al, and Li et al for a more general overview of benefits resulting from borrowing historical information in the analysis of the current trial. On the other hand, the use of highly informative priors in case of low sample size may lead to a domination of the prior, rather than the data, on the posterior inferences.…”

Section: Methodsmentioning

confidence: 99%

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

Gamba

Jacobs

Geys

et al. 2019

Pharmaceutical Statistics

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The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK‐PD) framework. While, at first sight, a Bayesian PK‐PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often‐overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed.

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Section: Methodsmentioning

confidence: 99%

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

Gamba

Jacobs

Geys

et al. 2019

Pharmaceutical Statistics

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“…For microarray data analysis, [ 21 , 22 ] argued that borrowing information across all genes might lead to over-correction. A better approach is to apply gene-specific or group-specific prior based on historic data to share information only across genes with similar variances.…”

Section: Methodsmentioning

confidence: 99%

Detecting differentially expressed genes for syndromes by considering change in mean and dispersion simultaneously

2018

BMC Bioinformatics

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BackgroundUsing next-generation sequencing technology to measure gene expression, an empirically intriguing question concerns the identification of differentially expressed genes across treatment groups. Existing methods aim to identify genes whose mean expressions differ among treatment groups by assuming equal dispersion across all groups. For syndromes, however, various combinations of gene expression alterations can result in the same disease, leading to greater heteroscedasticity in the biological replicates in the disease group compared to the normal group. Traditional methods that only consider changes in the mean will fail to fully analyze gene expression in such a scenario. In addition, sequencing technology is relatively expensive; most labs can only afford a few replicates per treatment group, which poses further challenges to reliably estimating the mean and dispersion under each treatment condition.ResultsWe designed an empirical Bayes method and a pooled permutation test to simultaneously consider the change in mean and dispersion across treatment groups. We further computed confidence intervals based on Bayes estimates to identify differentially expressed genes that are unique to each disease sample as well as those that are common across all disease samples. We illustrated our method by applying it to gene expression data from a large offspring syndrome experiment, which motivated this study. We compared our method to competing approaches through simulation studies that mimicked the real datasets to demonstrate the effectiveness of our proposed method.ConclusionsWe will show that, compared to popular methods that only aim to find the difference in the mean, our method can capture greater variation in the disease group to effectively identify differentially expressed genes for syndromes.

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“…In a recent study, the novel IPBT approach has shown to improve DE gene detection by using historical data (Li et al 2015). Although it has been shown that IPBT is reasonably robust when the historical data is rather noisy, its performance is somewhat sensitive to the quality of the corresponding historical data since IPBT uses historical SD directly.…”

Section: Methodsmentioning

confidence: 99%

“…For example, the diverse biological features of developmental genes, housekeeping genes, response genes are reflected in their expression profiles measured from many different conditions. As shown in the Figure 1 of Li et al (2015), there are substantial differences in terms of means and variances from gene to gene. Given heterogeneity in high-throughput genomics data, it is counter-productive for a highly and stably expressed housekeeping gene to borrow information from developmental genes with a bimodal expression pattern across experiments.…”

Section: Introductionmentioning

confidence: 99%

“…In a recent bioinformatics study, Li et al proposed a Bayesian strategy to incorporate historical data to help detect DE genes in microarray experiment (Li et al 2015). The main idea, behind the proposed method, named informative prior Bayesian test (IPBT), is the construction of gene-specific, informative prior for the variance of each gene using historical data.…”

Section: Introductionmentioning

confidence: 99%

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Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis

Qin

2017

Stat Biosci

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Modern high-throughput biotechnologies such as microarray and next generation sequencing produce a massive amount of information for each sample assayed. However, in a typical high-throughput experiment, only limited amount of data are observed for each individual feature, thus the classical ‘large p, small n’ problem. Bayesian hierarchical model, capable of borrowing strength across features within the same dataset, has been recognized as an effective tool in analyzing such data. However, the shrinkage effect, the most prominent feature of hierarchical features, can lead to undesirable over-correction for some features. In this work, we discuss possible causes of the over-correction problem and propose several alternative solutions. Our strategy is rooted in the fact that in the Big Data era, large amount of historical data are available which should be taken advantage of. Our strategy presents a new framework to enhance the Bayesian hierarchical model. Through simulation and real data analysis, we demonstrated superior performance of the proposed strategy. Our new strategy also enables borrowing information across different platforms which could be extremely useful with emergence of new technologies and accumulation of data from different platforms in the Big Data era. Our method has been implemented in R package “adaptiveHM”, which is freely available from https://github.com/benliemory/adaptiveHM.

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Bayesian inference with historical data-based informative priors improves detection of differentially expressed genes

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 11 publications

References 29 publications

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned

Detecting differentially expressed genes for syndromes by considering change in mean and dispersion simultaneously

Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis

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