Multi-way methods for understanding longitudinal intervention effects on bacterial communities

Abstract

BackgroundThis paper presents a strategy for statistical analysis and interpretation of longitudinal intervention effects on bacterial communities. Data from such experiments often suffers from small sample size, high degree of irrelevant variation, and missing data points. Our strategy is a combination of multiway decomposition methods, multivariate ANOVA, multi-block regression, hierarchical clustering and phylogenetic network graphs. The aim is to provide answers to relevant research questions, which are both statistically valid and easy to interpret. ResultsThe strategy is illustrated by analysing an intervention design where two mice groups were subjected to a treatment that caused inflammation in the intestines. Total microbiota in fecal samples was analysed at five time points, and the clinical end point was the load of colon cancer lesions. By using different combinations of the aforementioned methods, we were able to show that: The treatment had a significant effect on the microbiota, and we have identified clusters of bacteria groups with different time trajectories.  Individual differences in the initial microbiota had a large effect on the load of tumors, but not on the formation of early-stage lesions (flat ACFs).  The treatment resulted in an increase in Bacteroidaceae, Prevotellaceae and Paraprevotellaceae, and this increase could be associated with the formation of cancer lesions. ConclusionThe results show that by applying several data analytical methods in combination, we are able to view the system from different angles and thereby answer different research questions. We believe that multiway methods and multivariate ANOVA should be used more frequently in the bioinformatics fields, due to their ability to extract meaningful components from data sets with many collinear variables, few samples and a high degree of noise or irrelevant variation.

Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods

“…In addition, the hypothesis testing can be performed at the multivariate level through permutation tests. Both ASCA and FFMANOVA methods have successfully been applied on microbiome data [35][36][37][38]. Linear discriminant analysis effect size (LEfSe) is a stepwise approach that combines univariate analysis with multivariate discriminant analysis [39], but it is not well adapted to experimental designs with several multilevel factors.…”

Analysing microbiome intervention studies: Does choice of a statistical method affect biological interpretation?

“…In addition, the hypothesis testing can be performed at the multivariate level through permutation tests. Both ASCA and FFMANOVA methods have successfully been applied on microbiome data [35][36][37][38]. Linear discriminant analysis effect size (LEfSe) is a stepwise approach that combines univariate analysis with multivariate discriminant analysis [39], but it is not well adapted to experimental designs with several multilevel factors.…”

Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods

“…The contribution of each OTU can be quantified by the loadings or by partial least squares discriminant analysis (PLS-DA) for pairwise comparisons. ASCA has recently gained popularity in metabolomics [37][38][39], and both ASCA and FFMANOVA have successfully been applied to microbiome data [40][41][42][43][44].…”