Analysis and correction of compositional bias in sparse sequencing count data.

ABSTRACT:

Background

Count data derived from high-throughput deoxy-ribonucliec acid (DNA) sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. Commonly used count data normalization approaches like library size scaling/rarefaction/subsampling cannot correct for compositional or any other relevant technical bias that is uncorrelated with library size.

Results

We demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome thi

SUBMITTER: Kumar MS 

PROVIDER: S-EPMC6219007 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature