Effects of microbiome rare taxa filtering on statistical analysis

Abstract

<jats:title>Abstract</jats:title> <jats:p>Background: 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. Filtering is defined as removing taxa that are present in a small number of samples and have small counts in the samples where they are observed. This approach reduces 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 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 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 expressed across groups of samples, and random forest models to rank features with largest contribution towards disease classiffcation. 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 filtering and 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.</jats:p>

Tree mycorrhizal type and tree diversity shape the forest soil microbiota

“…The fungal and bacterial ASVs were filtered, and the ASV matrices were rarefied to 16,542 and 28,897 reads per sample, respectively. Furthermore, to avoid the potentially spurious taxa and to reduce the noise, taxa that were not present in at least 5% of the samples were removed in both fungal and bacterial datasets (Cao et al, 2021). Liner regression ('lm' function) and Mantel tests ('mantel' function in vegan) were used to test the effect of removal of low abundant taxa on alpha diversity indices and microbial community composition analyses, respectively.…”

Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome study

“…Filtering out the rare taxa is one common approach to deal with this problem. Cao et al (2021) [ 48 ] demonstrates that filtering reduces the complexity of microbiome data while preserving their integrity in downstream analysis and allows researchers to generate more reproducible and comparable results in microbiome data analysis. In Additional file 1 , Section S7, we simulated the longitudinal microbiome data from a zero-inflated NB distribution to evaluate whether and how sparsity or zero inflation affects the performance of MTA.…”