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Cited by 345Gut microbiota and healthMicrobial Community Ecology and PhysiologyGenomics and Phylogenetic Studies

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Analysis of microbial compositions: a review of normalization and differential abundance analysis

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Lin, Huang, Peddada, Shyamal Das·2020·npj Biofilms and Microbiomes
DOI 10.1038/s41522-020-00160-wPMC PMC7710733
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Abstract

Increasingly, researchers are discovering associations between microbiome and a wide range of human diseases such as obesity, inflammatory bowel diseases, HIV, and so on. The first step towards microbiome wide association studies is the characterization of the composition of human microbiome under different conditions. Determination of differentially abundant microbes between two or more environments, known as differential abundance (DA) analysis, is a challenging and an important problem that has received considerable interest during the past decade. It is well documented in the literature that the observed microbiome data (OTU/SV table) are relative abundances with an excess of zeros. Since relative abundances sum to a constant, these data are necessarily compositional. In this article we review some recent methods for DA analysis and describe their strengths and weaknesses.

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Fig. 1 Microbiome data is represented by relative abundances, thus differential abundance analysis should account for the bias introduced by across-sample variations in sampling fractions. Sampling fraction is defined as the ratio of expected abundance in a sample to the corresponding absolute abundance in the ecosystem, which could be empirically estimated by the ratio of library size to the microbial load. a Differences in sampling fractions introduce false negatives. In this toy example, the microbial load for subject A in a unit volume of the ecosystem (e.g. a unit volume of gut) is 8 (4 blue + 4 red), while for subject B it is 12 (6 blue + 6 red). However, the samples taken from subject A and B have the same library size 4 (2 blue + 2 red), same observed abundance as well as the same relative abundance of blue and red taxa. Thus, one may mistakenly conclude that the blue and red taxa are not differentially abundant between two ecosystems, which is not the case in the two ecosystems. This false negative conclusion is caused by differences in the sampling fractions in the two samples. The sampling fraction in sample A is 1/2 and for B it is 1/3. b Differences in sampling fractions introduces false positives. Consider another subject C, who has the microbial load of 16 (12 blue + 4 red) in a unit volume of ecosystem. Given the same library size in sample C (3 blue + 1 red) as sample A, one may mistakenly conclude that both blue and red are differentially abundant between ecosystems A and C, while in fact, only the blue taxon is differentially abundant. Thus a normalization method must account for differences in sampling fractions to avoid such erroneous conclusions.
Fig. 1 Microbiome data is represented by relative abundances, thus differential abundance analysis should account for the bias introduced by across-sample variations in sampling fractions. Sampling fraction is defined as the ratio of expected abundance in a sample to the corresponding absolute abundance in the ecosystem, which could be empirically estimated by the ratio of library size to the microbial load. a Differences in sampling fractions introduce false negatives. In this toy example, the microbial load for subject A in a unit volume of the ecosystem (e.g. a unit volume of gut) is 8 (4 blue + 4 red), while for subject B it is 12 (6 blue + 6 red). However, the samples taken from subject A and B have the same library size 4 (2 blue + 2 red), same observed abundance as well as the same relative abundance of blue and red taxa. Thus, one may mistakenly conclude that the blue and red taxa are not differentially abundant between two ecosystems, which is not the case in the two ecosystems. This false negative conclusion is caused by differences in the sampling fractions in the two samples. The sampling fraction in sample A is 1/2 and for B it is 1/3. b Differences in sampling fractions introduces false positives. Consider another subject C, who has the microbial load of 16 (12 blue + 4 red) in a unit volume of ecosystem. Given the same library size in sample C (3 blue + 1 red) as sample A, one may mistakenly conclude that both blue and red are differentially abundant between ecosystems A and C, while in fact, only the blue taxon is differentially abundant. Thus a normalization method must account for differences in sampling fractions to avoid such erroneous conclusions.CC BY · PubMed Central
Fig. 2 Rarefaction curves using the diet swap data68 at the genus level. The number of genera is 130, and the sample size is 222 (African American = 123, Native African = 99). x axis denotes the library size, and y axis represents the corresponding alpha diversity. Data are presented as mean values ± standard error (SE). It shows that regardless of the choice of diversity measures, as the increase of library size, the rarefaction curve starts to “level out” suggesting that the diversity of the samples has been fully observed.
Fig. 2 Rarefaction curves using the diet swap data68 at the genus level. The number of genera is 130, and the sample size is 222 (African American = 123, Native African = 99). x axis denotes the library size, and y axis represents the corresponding alpha diversity. Data are presented as mean values ± standard error (SE). It shows that regardless of the choice of diversity measures, as the increase of library size, the rarefaction curve starts to “level out” suggesting that the diversity of the samples has been fully observed.CC BY · PubMed Central
Fig. 3 Box plot of residuals between true sampling fraction and its estimate for each sample. In the box plot, the lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles). The median is represented by a solid line within the box. The upper whisker extends from the hinge to the largest value (maxima) no further than 1.5 times Interquartile Range (IQR, distance between the first and third quartiles) from the hinge, the lower whisker extends from the hinge to the smallest value (minima) at most 1.5 times IQR of the hinge. Data beyond the end of the whiskers are called “outlying” points. N = 90 samples examined over three study groups (denoted by circle, cross, and triangle, with 30 samples per group) and the data points are overlaid in each box. Each facet title indicates the normalization method and its variance is provided within parenthesis. The microbial absolute abundances in the ecosystem are generated from the log-normal distribution. By comparing residuals across different groups, an ideal box-plot should display a narrow height (i.e. smaller variability) and samples from different groups should be inter-mixed and not display any systematic separation. We note that all existing methods have larger variances compared to ANCOM-BC, and TSS has the largest variance. Except ANCOM-BC, UQ, and TMM, we see from the plot that circles, cross, and triangles are systematically separated, which indicates that ELib-UQ, ELib-TMM, CSS, MED, and TSS do not account for systematic bias due to differences in sampling fractions across groups.
Fig. 3 Box plot of residuals between true sampling fraction and its estimate for each sample. In the box plot, the lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles). The median is represented by a solid line within the box. The upper whisker extends from the hinge to the largest value (maxima) no further than 1.5 times Interquartile Range (IQR, distance between the first and third quartiles) from the hinge, the lower whisker extends from the hinge to the smallest value (minima) at most 1.5 times IQR of the hinge. Data beyond the end of the whiskers are called “outlying” points. N = 90 samples examined over three study groups (denoted by circle, cross, and triangle, with 30 samples per group) and the data points are overlaid in each box. Each facet title indicates the normalization method and its variance is provided within parenthesis. The microbial absolute abundances in the ecosystem are generated from the log-normal distribution. By comparing residuals across different groups, an ideal box-plot should display a narrow height (i.e. smaller variability) and samples from different groups should be inter-mixed and not display any systematic separation. We note that all existing methods have larger variances compared to ANCOM-BC, and TSS has the largest variance. Except ANCOM-BC, UQ, and TMM, we see from the plot that circles, cross, and triangles are systematically separated, which indicates that ELib-UQ, ELib-TMM, CSS, MED, and TSS do not account for systematic bias due to differences in sampling fractions across groups.CC BY · PubMed Central
Fig. 4 False Discovery Rate (FDR) and power comparisons using synthetic data. The FDR and power of various differential abundance (DA) analyses (two-sided) are shown in a, b, respectively. The number of taxa is set to be 200, and the sample size equals to 60, with 30 samples per group. The microbial absolute abundances in the ecosystem are generated from the log-normal distribution. The y-axis denotes patterns of proportion of differential abundant taxa, ranging from 0.05 to 0.25. The solid vertical line is the 5% nominal level of FDR, and the dashed vertical line denotes 5% nominal level plus one standard error (SE). Legend on the bottom indicates the color for each method and the normalization method is provided within parenthesis. By default, ANCOM-BC implements Holm-Bonferroni70 method and other DA methods implement BH procedure57 to adjust for multiple comparisons. By detecting differentially abundant taxa between two groups, results show that only ANCOM and ANCOM-BC control the FDR under the nominal level (5%) while maintaining power comparable to other methods. Gaussian model version of metagenomeSeq has highly inflated FDR, while the log Gaussian version has substantial loss of power, sometimes well below 5%.
Fig. 4 False Discovery Rate (FDR) and power comparisons using synthetic data. The FDR and power of various differential abundance (DA) analyses (two-sided) are shown in a, b, respectively. The number of taxa is set to be 200, and the sample size equals to 60, with 30 samples per group. The microbial absolute abundances in the ecosystem are generated from the log-normal distribution. The y-axis denotes patterns of proportion of differential abundant taxa, ranging from 0.05 to 0.25. The solid vertical line is the 5% nominal level of FDR, and the dashed vertical line denotes 5% nominal level plus one standard error (SE). Legend on the bottom indicates the color for each method and the normalization method is provided within parenthesis. By default, ANCOM-BC implements Holm-Bonferroni70 method and other DA methods implement BH procedure57 to adjust for multiple comparisons. By detecting differentially abundant taxa between two groups, results show that only ANCOM and ANCOM-BC control the FDR under the nominal level (5%) while maintaining power comparable to other methods. Gaussian model version of metagenomeSeq has highly inflated FDR, while the log Gaussian version has substantial loss of power, sometimes well below 5%.CC BY · PubMed Central
Fig. 5 Venn diagrams representing consistency of differentially abundant genera identified by ANCOM-BC, ANCOM, and DR. The global gut microbiota data60 is used to make the Venn diagram. The dataset contains 673 genera, with subjects from Malawi (MA, n1 = 114), USA (US, n2 = 317), and Venezuela (VEN, n3 = 99). We compare the absolute abundance of different genera for (1) Subjects who are less than or equal to 2 years old with sample size (MA:US:VEN) = (47:50:27) and (2) Subjects who are greater than 2 years old with sample size (MA:US:VEN) = (36:260:70). ANCOM-BC and ANCOM generally have large overlap of significant genera.
Fig. 5 Venn diagrams representing consistency of differentially abundant genera identified by ANCOM-BC, ANCOM, and DR. The global gut microbiota data60 is used to make the Venn diagram. The dataset contains 673 genera, with subjects from Malawi (MA, n1 = 114), USA (US, n2 = 317), and Venezuela (VEN, n3 = 99). We compare the absolute abundance of different genera for (1) Subjects who are less than or equal to 2 years old with sample size (MA:US:VEN) = (47:50:27) and (2) Subjects who are greater than 2 years old with sample size (MA:US:VEN) = (36:260:70). ANCOM-BC and ANCOM generally have large overlap of significant genera.CC BY · PubMed Central
Fig. 6 Waterfall plot visualizing coefficient (US: MA) for top 20 balances identified by gneiss using the global gut microbiota data60. We subset the dataset to subjects whose nationality is Malawi (MA) and USA (US), with sample size (MA:US) = (47:50) for the group of age ≤2 years old, and (MA:US) = (36:260) for the group of age >2 years old. The columns of the plot represent coefficients, and the rows of the plot represent balances. BH procedure57 was applied to correct for multiple comparisons, and coefficients with FDR corrected p-values < 0.05 are discarded.
Fig. 6 Waterfall plot visualizing coefficient (US: MA) for top 20 balances identified by gneiss using the global gut microbiota data60. We subset the dataset to subjects whose nationality is Malawi (MA) and USA (US), with sample size (MA:US) = (47:50) for the group of age ≤2 years old, and (MA:US) = (36:260) for the group of age >2 years old. The columns of the plot represent coefficients, and the rows of the plot represent balances. BH procedure57 was applied to correct for multiple comparisons, and coefficients with FDR corrected p-values < 0.05 are discarded.CC BY · PubMed Central

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  1. Gotelli, N. J. & Colwell, R. K. Estimating species richness. Biol. Divers. Front. Meas. Assess. 12, 39–54 (2011).

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Metadata

Authors
Lin, H., & Peddada, S. D.
Affiliations
National Institutes of Health (US)
Corresponding author
Peddada, Shyamal Das
ORCID
Venue
npj Biofilms and Microbiomes
Publisher
Springer Science and Business Media LLC
Published
2020-12-02
Volume
6
Issue
1
Pages
60-60
ISSN
2055-5008 (ISSN-L)
DOI
10.1038/s41522-020-00160-w
PMC
PMC7710733
OpenAlex
W3109541743
Cited by
341
References
68
Citations by year
2026: 57 · 2025: 73 · 2024: 67 · 2023: 77 · 2022: 55 · 2021: 11
FWCI
3.45
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Biofilms and Microbiomes (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
University of Pittsburgh
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
microbiome, differential abundance, compositional data, normalization, zero-inflated data, OTU table, sequencing read counts, human microbiome
MeSH terms
Gastrointestinal Microbiome, Bacteria, Humans, Models, Theoretical, Mouth, Phylogeny

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