|Title||Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences.|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Zhu, Anqi, Joseph G. Ibrahim, and Michael I. Love|
|Date Published||2019 Jun 01|
|Keywords||Likelihood Functions, Linear Models, Sequence Analysis, RNA, Software|
MOTIVATION: In RNA-seq differential expression analysis, investigators aim to detect those genes with changes in expression level across conditions, despite technical and biological variability in the observations. A common task is to accurately estimate the effect size, often in terms of a logarithmic fold change (LFC).RESULTS: When the read counts are low or highly variable, the maximum likelihood estimates for the LFCs has high variance, leading to large estimates not representative of true differences, and poor ranking of genes by effect size. One approach is to introduce filtering thresholds and pseudocounts to exclude or moderate estimated LFCs. Filtering may result in a loss of genes from the analysis with true differences in expression, while pseudocounts provide a limited solution that must be adapted per dataset. Here, we propose the use of a heavy-tailed Cauchy prior distribution for effect sizes, which avoids the use of filter thresholds or pseudocounts. The proposed method, Approximate Posterior Estimation for generalized linear model, apeglm, has lower bias than previously proposed shrinkage estimators, while still reducing variance for those genes with little information for statistical inference.AVAILABILITY AND IMPLEMENTATION: The apeglm package is available as an R/Bioconductor package at https://bioconductor.org/packages/apeglm, and the methods can be called from within the DESeq2 software.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
|Original Publication||Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences.|
|PubMed Central ID||PMC6581436|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
P30 ES010126 / ES / NIEHS NIH HHS / United States
R01 GM070335 / GM / NIGMS NIH HHS / United States
R01 HG009125 / HG / NHGRI NIH HHS / United States
Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences.