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Intervention studies on gut microbiota: Can ASCA compete with methods that are specifically tailored for microbial data?

Ingrid Måge1, Maryia Khomich2, Ida Rud1, Ingunn Berget1

  1. Nofima, Ås, Norway
  2. University of Bergen, Norway

e-mail: ingrid.mage@nofima.no

Gut microbiome has recently gained considerable attention, and its composition and diversity has been linked to several aspects of health and disease. Intervention studies are often used to investigate how the microbiome is affected by external factors such as treatments and diets. Data from such trials need to be analysed by multivariate ANOVA-like methods.

Microbiome data have some special features. The raw data typically consist of millions of DNA reads, which are converted to taxa counts or abundances through advanced bioinformatic pipelines. The data is zero-inflated, and a high number of rare taxa are usually removed before further analysis. The statistical analysis can be performed on either sequence counts, abundances, transformed abundances or distances.

We have compared the chemometric-based ANOVA-Simultaneous Component Analysis (ASCA) [1] to a range of other ANOVA-like methods that are frequently used for analysing microbial data, including: PERMANOVA [2], ANOSIM [3], SIMPER [3], ALDEx2 [4], ANCOM [5], LEfSe [6] and 50-50 MANOVA [7].

Comparisons were done using simulated data and five real dietary intervention studies. We have evaluated the methods abilities to detect community-level (multivariate) effects, as well as their abilities to identify differentially abundant bacterial groups. We report on the overall agreement between the methods, to assess to what extent the choice of method affects the results.

References

  1. Smilde, A. K. et al. ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data. Bioinformatics 21, 3043-3048; (2005).
  2. Anderson, M. J. A new method for non‐parametric multivariate analysis of variance. Austral Ecol. 26, 32-46 (2001).
  3. Clarke, K. R. Non‐parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117-143 (1993).
  4. Fernandes, A. D. et al. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15; (2014).
  5. Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663; (2015).
  6. Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60; (2011).
  7. Langsrud, Ø. 50–50 multivariate analysis of variance for collinear responses. J. R. Stat. Soc. - Ser. D Stat. 51, 305317; (2002).