Key Points

Introduction to multi-omics

Multi-omics data contains several data types measured from the same samples. Each data type looks at the samples from a different angle.
A table of taxa tells who is there, but not what the bacteria do or how the host responds. Multi-omics links the microbiome to its functions, its metabolites and the host, and points towards mechanisms.
Each data type is generated with its own method, and needs its own preparation. Microbiome counts are compositional; metabolites and biomarkers are not.
Tables computed from the same reads, such as taxa and pathways, are related by construction.
Multi-omics studies need matched samples, groups mixed across batches, and enough samples with all data types.
Exploratory methods test whether the data types are related, unsupervised methods find shared patterns, and supervised methods predict an outcome from all data types.

The HintikkaXO data has three data types from 40 rats: microbiota, metabolites and host biomarkers. The rats were fed a high-fat or a low-fat diet, with or without the prebiotic XOS.
A MultiAssayExperiment holds several experiments (here three TreeSEs), one shared table of sample information (colData()) and a sampleMap() that links them.
[[ picks one experiment, and $ accesses the sample information. getWithColData() returns an experiment together with the sample information.
upsetSamples() shows which samples have which data types, and intersectColumns() keeps the samples that have all of them.
Subsetting by samples keeps all experiments in sync. subsetByRow() subsets the features of each experiment.
sampleMap() links each column (colname) of each experiment to a subject (primary) in colData().
Each data type needs its own preparation: we agglomerate, filter and rclr-transform the compositional microbiota, replace values below the detection limit and log-transform the metabolites, and keep the biomarkers as they are.

Exploratory integration goes from the big picture to the details: global association, feature vs data type, and feature vs feature.
getMantel() tests whether two data types are related overall; getMiRKAT() finds the features of one data type that relate to the other as a whole.
getPairwiseAssociation() calculates the association of every pair of features; subsetByRow() keeps the result small enough to plot.
The Mantel test and MiRKAT are permutation or kernel tests: they compare similarities between samples instead of single features.
A shared factor, such as the diet, creates associations between data types. Check associations within each group (group.by). Associations are hypotheses, not proof that a bacterium produces a metabolite.

Unsupervised integration methods find the main patterns that several data types share, without knowing the study groups.
Use rclr with impute = FALSE, so that the integration methods handle the zeros themselves.
Joint RPCA (addJointRPCA()) draws one map of the samples from two or more data types; plotJointRPCA() shows the samples and the features that drive the map.
MOFA is a factor model: a few hidden factors, shared by all data types or specific to one, explain the data. Standardize the features, and ask for few factors when there are few samples.
plot_variance_explained() shows how much of each data type each factor explains, and plot_top_weights() the features that drive it.
Joint RPCA and MOFA show the shared patterns in different ways; in our data they agree.
In our study, the main shared pattern is the fat in the diet. Smaller effects, such as XOS, can be hidden by larger ones.

Supervised integration predicts an outcome from several data types. Early fusion joins the tables, late fusion combines the predictions of separate models, and intermediate fusion combines what separate models learned.
IntegratedLearner() trains models on a MultiAssayExperiment and reports cross-validated AUCs for each data type and for early and late fusion, plus the weight of each data type.
DIABLO (block.splsda()) selects a few features from each data type that together tell the groups apart and are correlated with each other.
Always evaluate a supervised model with cross-validation. Combining data types does not create a signal that is not in the data.

Exploratory methods test whether data types are related, unsupervised methods find shared patterns, and supervised methods predict an outcome.
Choose the group of methods by the research question, and look for results that are consistent across methods.
Integrating data types does not create a signal that is not in the data.