Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences.

ABSTRACT:

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 li

SUBMITTER: Zhu A 

PROVIDER: S-EPMC6581436 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature