Key Points

Introduction to microbiome data

  • A microbiome study goes from data generation and pre-processing to the data object, data processing and statistical analysis. This lesson covers the data object, data processing and statistical analysis steps; data integration is covered in the follow-up multi-omics lesson.
  • Microbiome data consists of an abundance table (taxa × samples), taxonomy, sample information and often a phylogenetic tree.
  • The number of reads per sample depends on sequencing depth, so we usually compare relative abundances.
  • Relative abundances are compositional: they add up to 100 %, so a change in one taxon changes the shares of all others.
  • Microbiome data contains many zeros and many more taxa than samples.
  • Alpha diversity, beta diversity and differential abundance are the standard first analyses of a microbiome study, and for some studies they are enough.
  • This lesson follows one study, on colorectal cancer, from data to results. Other differences between the groups, such as where people live, can confound the comparison.

From samples to tables

  • Choices made during data generation, such as sample storage, DNA extraction and batches, leave a mark on the data. Read how the samples were collected and processed before you analyse them.
  • Pre-processing turns raw reads (FASTQ files) into tables of taxa. It is usually done outside R, before the analysis that this lesson covers.
  • The numbers in the abundance table describe the sequenced DNA, and their total depends on the sequencing depth.
  • The outputs go into the parts of a TreeSE: the abundance table into assay, the taxonomy into rowData, the sample information into colData and the tree into rowTree.
  • mia has import functions, such as importQIIME2(), importMetaPhlAn() and convertFromPhyloseq(), that build a TreeSE directly from pipeline output.

RStudio project and experimental data

  • Use an RStudio project to keep the data, code and results of an analysis together.
  • Use download.file() to download data from the internet, and file.path() to build file paths.
  • The main packages of this lesson are mia and miaViz from the OMA framework; later episodes also use ape, scater, bluster, daa, maaslin3 and mikropml.

Importing data into TreeSummarizedExperiment

  • A TreeSummarizedExperiment keeps the abundance table, taxonomy, sample information and tree together and in sync.
  • Rows are taxa and columns are samples.
  • assay(), rowData() and colData() access the abundance table, the taxonomy and the sample information.
  • Subsetting with [rows, columns] keeps all parts of the object in sync.
  • plotBarplot() gives a quick overview of the study groups. In our study, the groups differ in more than disease: most healthy people come from Bhopal.

Exploring and preparing microbiome data

  • Library sizes differ between samples, so we compare relative values instead of raw counts.
  • agglomerateByRank() combines taxa into a higher taxonomic rank.
  • (Bonus) agglomerateByRanks() stores the data at every rank as alternative experiments in one object; altExp() picks one, and swapAltExp() makes one the main data.
  • transformAssay() adds transformed tables, such as relative abundance and robust CLR (rclr), and keeps the original counts.
  • rclr compares each taxon to the average taxon of its sample on a log scale. It fills in the zeros with estimates, so single rclr values are estimates, not measurements.
  • Which table to use depends on the method and its assumptions. The rclr values are closer to a normal distribution than the counts.
  • plotAbundance() shows the community composition of each sample, and plotBoxplot() the abundance of a single taxon by group.
  • subsetByPrevalent() removes rare taxa. Use it for differential abundance, but not for diversity.
  • In our study, one species, Prevotella copri, dominates the gut of many healthy people.

Alpha diversity

  • Alpha diversity describes the diversity within a sample: richness (how many taxa), evenness (how equal), both (Shannon), or the share of the tree of life covered (Faith). The indices give complementary information.
  • addAlpha() calculates diversity indices and adds them to colData.
  • plotBoxplot() and daa::getWilcoxonTest() compare alpha diversity between two groups. The Wilcoxon test does not assume normally distributed values.
  • In our study, people with CRC have a higher diversity, partly because the gut of many healthy people is dominated by one species. Higher diversity is not automatically healthier.
  • Check for confounding: in our study, the CRC patients are also older than the healthy people.

Beta diversity

  • Beta diversity compares whole communities between samples, using a dissimilarity such as Bray–Curtis.
  • addMDS() calculates a PCoA from any dissimilarity, runPCA() a PCA from rclr values, and plotOrdination() draws them as a map of the samples. PCA and PCoA are complementary, and PCA loadings show which taxa drive the axes.
  • UniFrac uses the phylogenetic tree, so that closely related taxa count as similar. Unweighted UniFrac uses presence and absence; weighted UniFrac also uses the abundances.
  • PERMANOVA (getPERMANOVA()) tests whether groups differ in composition; R2 tells how much of the differences the group explains. Always check whether the groups differ in spread, because this affects the PERMANOVA result.
  • dbRDA (addRDA()) takes other variables into account, which is important when groups differ in more than one way.
  • (Bonus) Clustering (addCluster()) finds groups of similar samples without using the sample information. Cluster the data itself, never the ordination axes, and use the ordination only to show the clusters.
  • In our study, the gut microbiome of people with CRC differs from that of healthy people, even when the location is taken into account. The samples fall into a Prevotella and a non-Prevotella community type, which is strongly linked to the disease.

Differential abundance and prevalence analysis

  • Differential abundance analysis tests whether the differences we see in plots are statistically significant.
  • At its core, DAA is a simple test: a t-test is a linear model with one two-group variable, and a linear model can add covariates, such as the location. Most DAA methods are based on this under the hood.
  • Testing many taxa produces false positives, so we use q-values that are corrected for multiple testing.
  • Dedicated methods, such as MaAsLin 3, extend the linear model to take the specific nature of microbiome data into account: relative values, many zeros and noisy abundances.
  • A taxon can differ in abundance, in prevalence (how often it is present), or both. MaAsLin 3 tests both.
  • Look at the data behind the results, and treat significant taxa as candidates for further study.
  • In our study, Prevotella copri and butyrate-producing species are less abundant in people with CRC, and Flavonifractor plautii is found much more often in CRC.

Machine learning: predicting an outcome

  • Machine learning asks how well we can predict an outcome, such as a disease, from features, such as taxon abundances.
  • Always evaluate a model on samples it has not seen: cross-validation on the training data and a separate test set.
  • Hyperparameters, such as mtry of a random forest, are chosen by cross-validation on the training data only.
  • mikropml works directly with a TreeSE: run_ml() trains, tunes and tests a model in one call, and preprocess_data() prepares the data when a model needs it. Random forests work on raw counts.
  • The AUC measures how well the predictions separate the groups. With a small test set, it varies a lot between random splits.
  • Feature importance shows which taxa the model relies on. In our study, many of them, such as Roseburia faecis, also differ in abundance.
  • Confounders and small sample sizes can make a model look better than it is; independent validation is needed.

Capstone: analyse a new dataset

  • The same workflow (explore, alpha diversity, beta diversity, differential abundance) works for new datasets, including amplicon data.
  • With real read counts and very different library sizes, rarefaction is an option for richness.
  • Include technical variables such as the cohort or batch in the models.
  • Non-significant results are results too. Report them together with the sample size and the limitations.
  • A short written report forces you to state the question, the methods, the results and the limitations clearly.

Next steps

  • A typical microbiome analysis goes from import and preparation to alpha diversity, beta diversity and differential abundance.
  • These three analyses are what most studies report. They are sometimes enough on their own, and they are always the first steps before more advanced analyses.
  • The OMA book is the place to continue learning, and the follow-up multi-omics lesson shows how to combine the microbiome with other data types.