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FEAST: fast expectation-maximization for microbial source tracking

Abstract

A major challenge of analyzing the compositional structure of microbiome data is identifying its potential origins. Here, we introduce fast expectation-maximization microbial source tracking (FEAST), a ready-to-use scalable framework that can simultaneously estimate the contribution of thousands of potential source environments in a timely manner, thereby helping unravel the origins of complex microbial communities (https://github.com/cozygene/FEAST). The information gained from FEAST may provide insight into quantifying contamination, tracking the formation of developing microbial communities, as well as distinguishing and characterizing bacteria-related health conditions.

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Data availability

All of the datasets analyzed in this paper are public and can be referenced at the following accession numbers: The first dataset was collected and studied by Backhed et al.16 (accession number ERP005989). The second dataset was collected and studied by Lax et al.15 (accession number ERP005806). The third dataset was collected and studied by Knights et al.10 (data from this study are stored in https://github.com/danknights/sourcetracker). The fourth dataset was collected and studied by McDonald et al.12 (accession number ERP012810) and the American Gut Project30 (EBI project number PRJEB11419). The fifth dataset was collected and studied by Taur et al.18 (data from this study are stored in http://www.ncbi.nlm.nih.gov/sra). In our simulations we used the Earth microbiome project (ftp://ftp.microbio.me/emp/release1/otu_tables/closed_ref_greengenes/).

Code availability

Code is available at https://github.com/cozygene/FEAST

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

We thank S. Mukherjee for insightful comments on the manuscript. This research was partially supported by European Research Council under the European Union’s Horizon 2020 research and innovation program, project number 640384. This work was partially supported by the National Science Foundation (grant number 1705197). T.A.J. was supported by National Science Foundation (grant no. DGE-1644869).

Author information

L.S. and E.H. conceived the statistical model. L.S. designed the algorithm and software, and performed computational experiments. L.S., M.T., T.A.J. and L.B. wrote the manuscript. O.F. and D.B. contributed to writing the manuscript. T.A.J. and M.T. contributed to algorithm design. M.T. and L.B contributed to the computational experiments. I.M., I.P. and E.H. supervised the project.

Competing interests

The authors declare no competing interests.

Correspondence to Eran Halperin.

Integrated supplementary information

  1. Supplementary Figure 1 The accuracy of FEAST and SourceTracker using data-driven synthetic mixtures.

    The accuracy of FEAST and SourceTracker on simulated data. Each simulation was performed using 10 real source environments and simulated sinks. The x-axis is average Jensen-Shannon divergence value across known sources. The y-axis represents correlation across all source environments between true and estimated mixing proportions, measured by (a) the squared Pearson correlation coefficient averaged across sources, and (b) the squared Spearman correlation coefficient averaged across sources.

  2. Supplementary Figure 2 Evaluation of FEAST and SourceTracker through varying levels of sequencing depth.

    Evaluation of FEAST and SourceTracker through varying levels of sequencing depth. Similarity of sequences remained constant (Jensen-Shannon divergence = 0.95, trivial to disambiguate), while sequencing depth was set to vary in the range 100–10,000.

  3. Supplementary Figure 3 The expected variance in FEAST's output.

    The expected variance in FEAST's output using the dataset from McDonald et al. We used the gut microbiome of one, randomly selected, ICU patient as a sink, and the sources considered by McDonald et al.: 126 healthy controls, 126 samples of mammalian corpse decomposition, 126 samples of the gut from healthy children, and 126 samples from indoor house surfaces. By repeating this analysis 100 times and calculating the standard deviation of each source we demonstrate that the variance in FEAST’s output is very small (that is., sd(dust) = 7.7e-05, sd(healthy adults' feces) = 0.01, sd(healthy children's feces) = 0.01,sd(soil) = 5e-05, sd(unknown) = 8.5e-05).

  4. Supplementary Figure 4 The effect of noisy samples among sources on prediction accuracy.

    The effect of noisy samples among sources on prediction accuracy (that is., estimation of the known and unknown sources). As we increase the number of samples per source, FEAST’s prediction accuracy improves, however this effect is moderate (squared Pearson correlation ranges from 0.9–0.99, Jensen-Shannon divergence values range from 0.87–0.92).

  5. Supplementary Figure 5 The source proportions using SourceTracker.

    SourceTracker estimations of source contribution (the gut microbiome of mother, infant at 4 months and infant at birth) to the gut microbiome of 12-month-old infants. According to SourceTracker differences between C-section (n = 15) and Vaginally-delivered (n = 83) infants in terms of maternal contribution are not significant (two-sided t-test p-value = 0.6408). Box plots indicate the median (central lines), interquartile range (hinges), and the 5th and 95th percentiles (whiskers).

  6. Supplementary Figure 6 Detecting contamination in lab-settings.

    FEAST and SourceTracker report consistent proportions of contamination, despite minor discrepancies in a lab-setting (left: keyboard, right: Counter). Estimates on the top row were reported by SourceTracker and estimates on the bottom row were reported by FEAST.

  7. Supplementary Figure 7 Gut microbiome samples from ICU patients are not reminiscent of gut samples from healthy individuals.

    Gut samples from ICU patients are not reminiscent of gut samples from healthy individuals. We used the gut microbiome of each ICU patient (at discharge or after 10 days) as a sink, and the sources considered by the original study (McDonald et al. 2016): 126 samples from the American Gut Project (healthy controls), 126 samples of mammalian corpse decomposition, 126 samples of the gut from healthy children (Global Gut study), and 126 samples from indoor house surfaces.

  8. Supplementary Figure 8 Unknown source distribution across sink samples (ICU patients vs. healthy individuals).

    The distribution of the unknown source across sink samples—healthy individuals and ICU patients (n = 100).

  9. Supplementary Figure 9 Distinguishing between ICU patients and healthy individuals.

    The receiver operating characteristic curve (ROC curve) using FEAST, Weighted UniFrac, Bray-curtis and Jensen Shannon divergence to classify healthy individuals and ICU patients with dysbiosis. FEAST AUC = 0.91, Weighted UniFrac AUC = 0.78, Jensen Shannon divergence AUC = 0.87, Bray-curtis AUC = 0.86.

  10. Supplementary Figure 10 The source contribution across maternal samples.

    Distribution of the median random maternal rank in two scenarios: (a) all maternal and early infant samples (from all the infants in the study) were considered as potential sources (n = 293 sources), and (b) only the maternal samples were considered as potential sources (n = 98 sources). In both scenarios samples taken from infants at age 12 months were considered as sinks (n = 98 sinks). The red vertical line in each figure corresponds to the actual median rank of the maternal contribution.

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    Supplementary Figs. 1–10, Supplementary Tables 1 and 2 and Supplementary Notes

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Fig. 1: Methods comparison.
Fig. 2: Running time comparison to current state-of-the-art.
Fig. 3: FEAST estimations of source contribution to the sink; that is, gut microbiome of focal infant at 12-months of age.
Fig. 4: The proportion of the unknown sources in kitchen counter samples using FEAST and SourceTracker.
Fig. 5: The receiver operating characteristic curve using FEAST, weighted UniFrac and Jensen–Shannon divergence to classify healthy individuals and patients in ICU with dysbiosis.
Fig. 6: Significant differences in the distribution of the unknown source between sink samples before and during the first event of intestinal domination across 94 patients undergoing allo-HSCT.
Supplementary Figure 1: The accuracy of FEAST and SourceTracker using data-driven synthetic mixtures.
Supplementary Figure 2: Evaluation of FEAST and SourceTracker through varying levels of sequencing depth.
Supplementary Figure 3: The expected variance in FEAST's output.
Supplementary Figure 4: The effect of noisy samples among sources on prediction accuracy.
Supplementary Figure 5: The source proportions using SourceTracker.
Supplementary Figure 6: Detecting contamination in lab-settings.
Supplementary Figure 7: Gut microbiome samples from ICU patients are not reminiscent of gut samples from healthy individuals.
Supplementary Figure 8: Unknown source distribution across sink samples (ICU patients vs. healthy individuals).
Supplementary Figure 9: Distinguishing between ICU patients and healthy individuals.
Supplementary Figure 10: The source contribution across maternal samples.