Normalization and microbial differential abundance strategies depend upon data characteristics

By Sophie J. Weiss, Zhenjiang Xu, Shyamal Das Peddada, Amnon Amir, Kyle Bittinger, Antonio Gonzalez, Catherine A. Lozupone, Jesse R. Zaneveld, Yoshiki Vazquez-Baeza, Amanda Birmingham and 2 more. Microbiome, 2017

1,740 citations recorded by the source corpus.

Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate. These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.

Subfields: Clinical Biochemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5415ab67f45cff058ddb86a65d073da7714b91c0

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Citations in the corpus

Comparative analysis of diversity and environmental niches of soil bacterial, archaeal, fungal and protist communities reveal niche divergences along environmental gradients in the Alps (2021, methodology): “To assess beta diversity of microbial communities, each zOTU table was transformed to relative abundance (Weiss et al., 2017) using the ratio of read counts and Bray-Curtis distances were calculated using the phyloseq and vegan R packages.”
LinDA: linear models for differential abundance analysis of microbiome compositional data (2021, methodology): “Since the presence/absence of a taxon strongly depends on the library size, the differential library size will confound the two-sample comparison, especially for those rare taxa [25].”
Cultivating the Macroalgal Holobiont: Effects of Integrated Multi-Trophic Aquaculture on the Microbiome of Ulva rigida (Chlorophyta) (2020, methodology): “This strategy avoids the increase of false negatives (lower sensitivity) caused by rarefying normalization as well as false positives generated by comparing abundances directly across samples (McMurdie and Holmes, 2014; Weiss et al., 2017b).”
Microbiome differential abundance methods produce different results across 38 datasets (2022, methodology): “These were run using 100 replicates in only 3 of the 8 datasets (Freshwater – Arctic, Soil – Blueberry, Human - OB (1)) with 100 ALDEx2 replications also being run in the Human - HIV (3) dataset.”
Rarefaction, Alpha Diversity, and Statistics (2017, background): “Based on these subsamples of equal size, diversity metrics can be calculated that can contrast ecosystems “fairly,” independent of differences in sample sizes (Weiss et al. 2017).”
MicrobiomeAnalyst 2.0: comprehensive statistical, functional and integrative analysis of microbiome data (2023, neutral): “In differential abundance analysis, se v eral benchmar k studies hav e shown inconsistencies among methods de v eloped specifically for microbiome da ta, and tha t common RN Aseq anal ysis methods are robust and perform well ( 17 , 18 ).”
Metabarcoding: A powerful tool to investigate microbial communities and shape future plant protection strategies (2017, background): “Since not all individual samples will have the same number of reads, variations in library size must be normalized before conducting further downstream analyses (Weiss et al., 2017).”
American Gut: an Open Platform for Citizen Science Microbiome Research (2018, neutral): “Group 2 contained samples from individuals answering “antibiotic use within last week” ( n (cid:2) 56) and “I have not taken antibiotics in the past year” ( n (cid:2) 115) to the main American Gut Project survey question “I have taken antibiotics in the last ____.” for the antibiotic history study,…”
Microbiome Datasets Are Compositional: And This Is Not Optional (2017, methodology): “ANCOM is being incorporated into the popular QIIME suite of microbiome analysis tools (Weiss et al., 2017).”