Microbiome differential abundance analysis methods for two groups are well-established in the literature. However, many microbiome studies involve more than two groups, sometimes even ordered groups such as sta
Conclusions: Differential abundance tests yielded varied results. Using one method on one dataset may find true associations, but may also detect non-reproducible signals, adding to inconsistency in the literature. To help lower false positives, one might analyze data with two or more DA methods ...
Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010). CAS PubMed PubMed Central Google Scholar Sczyrba, A. et al. Critical assessment of metagenome interpretation–a benchmark of computational metagenomics software. Nat. Methods 14, 1063–1071 (2017). CAS ...
there is not even resolution in the study to say anything meaningful about that microbe in the context of differential abundance analysis. The--min-feature-countfilter is appliedafterthe--min-sample-countis applied, so it's possible for (for example) a sample to get filtered out which in tur...
46,54,55 Interestingly, enrichment analysis using differential KEGG genes identified by other differential abundance methods cannot identify these two pathways simultaneously (Figure SB.2). In this example, we use MicrobiotaProcess for a differential abundance analysis, and then use the in-house ...
For differential abundance comparison analysis at the phylum level, the abundance of Bacteroidetes was significantly higher in the rumen of LL cows, while that of Proteobacteria was significantly higher in the rumen of HH cows (adjusted P < 0.05, Figure S3). At the species level, 15 ...
For the differential abundance comparison analysis of archaea, phyla Euryarchaeota (93.98 ± 0.85%, the most abundant archaeal phylum), and genera Methanobrevibacter (79.06 ± 1.76%, the most abundant archaeal genus) were significantly higher in the A16Con and A30Con groups than those ...
(different) MHC-I alleles, including the differential abundance of certain bacterial taxa. While we found no effect of MHC diversity orTLR3genotype on the GM, we did find a positive association between bacterial GM diversity and individual genome-wide heterozygosity. Lastly, GM characteristics were...
After data preprocessing (unsupervised abundance and prevalence filtering, Fig.1a and the “Methods” section), univariate associations of single species with the disease are computed using the non-parametric Wilcoxon test (which has been shown for metagenomic data to reliably control the false discover...
Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol. 2009; 5(4):1000352. doi:10.1371/journal.pcbi.1000352. Article CAS Google Scholar Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene ...