Beware to ignore the rare: how imputing zero-values can improve the quality of 16S rRNA gene studies results

BMC Bioinformatics

Research

Giacomo Baruzzo¹  Barbara Di Camillo²  Ilaria Patuzzi³ 

[1] Department of Information Engineering, University of Padova, Padua, Italy;Department of Information Engineering, University of Padova, Padua, Italy;CRIBI Biotechnology Centre, University of Padova, Padua, Italy;Department of Comparative Biomedicine and Food Science, University of Padova, Padua, Italy;Department of Information Engineering, University of Padova, Padua, Italy;Microbial Ecology Unit, Istituto Zooprofilattico Sperimentale Delle Venezie, Padua, Italy;Research & Development Division, EuBiome S.R.L., Padua, Italy;

关键词: Zero-imputation;    Sparsity;    Normalization;    Count preprocessing;    16S rDNA-Seq;    Count data;    Count simulation;    Benchmarking;   

DOI  :  10.1186/s12859-022-04587-0

 received in 2022-01-26, accepted in 2022-01-27,  发布年份 2022

来源: Springer

【 摘 要 】

Background16S rRNA-gene sequencing is a valuable approach to characterize the taxonomic content of the whole bacterial population inhabiting a metabolic and spatial niche, providing an important opportunity to study bacteria and their role in many health and environmental mechanisms. The analysis of data produced by amplicon sequencing, however, brings very specific methodological issues that need to be properly addressed to obtain reliable biological conclusions. Among these, 16S count data tend to be very sparse, with many null values reflecting species that are present but got unobserved due to the multiplexing constraints. However, current data workflows do not consider a step in which the information about unobserved species is recovered.ResultsIn this work, we evaluate for the first time the effects of introducing in the 16S data workflow a new preprocessing step, zero-imputation, to recover this lost information. Due to the lack of published zero-imputation methods specifically designed for 16S count data, we considered a set of zero-imputation strategies available for other frameworks, and benchmarked them using in silico 16S count data reflecting different experimental designs. Additionally, we assessed the effect of combining zero-imputation and normalization, i.e. the only preprocessing step in current 16S workflow. Overall, we benchmarked 35 16S preprocessing pipelines assessing their ability to handle data sparsity, identify species presence/absence, recovery sample proportional abundance distributions, and improve typical downstream analyses such as computation of alpha and beta diversity indices and differential abundance analysis.ConclusionsThe results clearly show that 16S data analysis greatly benefits from a properly-performed zero-imputation step, despite the choice of the right zero-imputation method having a pivotal role. In addition, we identify a set of best-performing pipelines that could be a valuable indication for data analysts.

【 授权许可】

CC BY   
© The Author(s) 2022

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