Analysis and correction of compositional bias in sparse sequencing count data

• Published in: BMC genomics Vol. 19; no. 1; pp. 799 - 23
• Main Authors: Kumar, M. Senthil, Slud, Eric V., Okrah, Kwame, Hicks, Stephanie C., Hannenhalli, Sridhar, Corrada Bravo, Héctor
• Format: Journal Article
• Language: English
• Published: London BioMed Central 06.11.2018; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Analysis; Animal Genetics and Genomics; Bayes Theorem; Bayesian analysis; Bias; Biomedical and Life Sciences; Compositional bias; Computational Biology - methods; Count data; Data integration; Deoxyribonucleic acid; DNA; DNA sequencing; Empirical analysis; Empirical Bayes; Experiments; Gene expression; Genomics; High-Throughput Nucleotide Sequencing - methods; Hypothesis testing; Life Sciences; Metagenomics; Metagenomics - methods; Methodology; Methodology Article; Microarrays; Microbial Genetics and Genomics; Microbiota; Normalization; Oceans; Plant Genetics and Genomics; Proteomics; Rarefaction; RNA, Ribosomal, 16S - genetics; Scaling; Science; Selection bias; Transcriptomic methods; Empirical Bayes; Metagenomics; Count data; Absolute abundance; Data integration; Spike-in; Normalization; Compositional bias; scRNAseq
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-018-5160-5

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 this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it. Conclusions Compositional bias, induced by the sequencing machine, confounds inferences of absolute abundances. We present a normalization technique for compositional bias correction in sparse sequencing count data, and demonstrate its improved performance in metagenomic 16s survey data. Based on the distribution of technical bias estimates arising from several publicly available large scale 16s count datasets, we argue that detailed experiments specifically addressing the influence of compositional bias in metagenomics are needed.