Effects of Rare Microbiome Taxa Filtering on Statistical Analysis

Abstract

Background: The accuracy of microbial community detection in 16S rRNA marker-gene and metagenomic studies suffers from contamination and sequencing errors that lead to either falsely identifying microbial taxa that were not in the sample or misclassifying the taxa of DNA fragment reads. Removing contaminants and filtering rare features are two common approaches to deal with this problem. While contaminant detection methods use auxiliary sequencing process information to identify known contaminants, filtering methods remove taxa that are present in a small number of samples and have small counts in the samples where they are observed. The latter approach reduces the extreme sparsity of microbiome data and has been shown to correctly remove contaminant taxa in cultured “mock” datasets, where the true taxa compositions are known. Although filtering is frequently used, careful evaluation of its effect on the data analysis and scientific conclusions remains unreported. Here, we assess the effect of filtering on the alpha and beta diversity estimation as well as its impact on identifying taxa that discriminate between disease states.Results: The effect of filtering on microbiome data analysis is illustrated on four datasets: two mock quality control datasets where the same cultured samples with known microbial composition are processed at different labs and two disease study datasets. Results show that in microbiome quality control datasets, filtering reduces the magnitude of differences in alpha diversity and alleviates technical variability between labs while preserving the between samples similarity (beta diversity). In the disease study datasets, DESeq2 and linear discriminant analysis Effect Size (LEfSe) methods were used to identify taxa that are differentially abundant across groups of samples, and random forest models were used to rank features with the largest contribution toward disease classification. Results reveal that filtering retains significant taxa and preserves the model classification ability measured by the area under the receiver operating characteristic curve (AUC). The comparison between the filtering and the contaminant removal method shows that they have complementary effects and are advised to be used in conjunction.Conclusions: Filtering reduces the complexity of microbiome data while preserving their integrity in downstream analysis. This leads to mitigation of the classification methods' sensitivity and reduction of technical variability, allowing researchers to generate more reproducible and comparable results in microbiome data analysis.

Phylogeny- and Abundance-Based Metrics Allow for the Consistent Comparison of Core Gut Microbiome Diversity Indices Across Host Species

“…Moreover, we show that prevalence filtering can have additional disadvantages such as altering sample diversity rankings in a manner that may not be consistent across studies. Therefore, whilst filtering rare taxa can reduce technical bias ( Cao et al, 2021 ), heavier filtering parameters may not always generate ecologically meaningful results that are comparable across the literature (although see Ainsworth et al, 2015 ; Grieneisen et al, 2017 ; Russell et al, 2019 ), and variation in sequencing depth after filtering may also bias weighted alpha and beta diversity scores unless further normalization methods are applied (e.g., Silverman et al, 2017 ; Beule and Karlovsky, 2020 ). Our results indicate that diversity measures that account for both abundance and phylogeny (BWPD and Weighted Unifrac, for alpha and beta diversity, respectively) are insensitive to prevalence thresholds, and therefore represent the common core microbiome without the need for filtering.…”

“…Quality filtering by excluding very low prevalence taxa (e.g., that occur in just a few samples) reduces effects of sequencing error (Bokulich et al, 2013;Callahan et al, 2016;Amir et al, 2017), although it is argued this method also excludes rare yet real taxa and therefore can bias results in other ways (Kozich et al, 2013;Jousset et al, 2017;Schloss, 2020). Statistical filtering, on the other hand, removes rare yet resident taxa (i.e., they are not due to sequencing error) and is recommended for many analyses, such as network and differential abundance analysis to increase their reliability (Röttjers and Faust, 2018;Cougoul et al, 2019;Cao et al, 2021). Statistical filters generally apply higher prevalence thresholds than quality filters, with taxa below ∼20% prevalence often being limited in their statistical testability, although this number is dependent on sample size (Cougoul et al, 2019).…”

Harnessing the Power of Metabarcoding in the Ecological Interpretation of Plant-Pollinator DNA Data: Strategies and Consequences of Filtering Approaches

“…Specifically, we focused on the analysis of DNA metabarcoding of pollen in the framework of plant-pollinator interactions, being aware that the outputs of our investigation could be extended to the other typologies of DNA metabarcoding-based studies. Although the issue of removing false positives and rare taxa or features is quite neglected in the scientific literature in relation to the bioinformatic pipeline (but see [28,30]), the choices made when analysing a HTS output could generate relevant effects on the community composition, species richness, and species interactions. These aspects would deeply impact the ecological outcomes of the investigated system.…”

“…Given the extreme sensitivity of DNA metabarcoding, it is crucial to filter out false positives and contaminants, which could significantly alter the reconstruction of samples composition. Moreover, rare features or taxa should be treated consciously during the postsequencing bioinformatics processing and possibly removed, depending on the study aims and the required sensitivity of the analysis [27][28][29][30]. However, the resulting species composition of a sample could be biased by the disapplication or misapplication of cut-off thresholds.…”

Accurate Five-category Classification for Colorectal Cancer Using Gut Microbiome 16S rRNA Sequencing

“…Similar to existing studies [24][25][26][27][28] , 16S rRNA gene (V3-V4 region) sequencing was used to investigate the microbiota content, although it may not be possible to achieve specieslevel differentiation. We formalized the task of CRC or adenoma prediction as a classification problem and focused on operational classification units (OTUs).…”