Multiomics workshop 2026

Quentin D. Read & Matt Burnham

What is this talk?

  • Intended for people who have multiomics data but are not experienced in analyzing it
  • Resources for further learning about omics analysis
  • Conceptual overview of how omics analysis works

Resources for further learning about multiomics

ASA Short Course

mixOmics courses

Swedish National Bioinformatics Infrastructure courses

Multiomics Integration: overview of the concepts

What do multiomics data look like?

  • We have more than one omics dataset
    • Genome
    • Methylome
    • Metabolome
    • Transcriptome
    • Microbiome
  • Either organism-wide or single-cell
  • We have metadata about the organisms or cells
    • Experimental treatment
    • Individual-level variables (age, sex, body weight, etc.)
    • Individual-level outcomes (disease status, mortality, etc.)
    • Cell-level variables (tissue type, etc.)

What research questions can we answer?

  • What are the relationships between the different omics datasets?
  • How well do the omics datasets, individually or combined, predict outcomes?

Supervised or unsupervised?

  • Supervised we have some kind of outcome variable (example: disease status) and we are trying to figure out which combinations of features in the different omics datasets predict it
  • Unsupervised we don’t have an outcome, we are just exploring relationships between the datasets

Descriptive or predictive? (within supervised methods)

  • Descriptive we want to find weights for the variables to optimally separate the classes
    • many different criteria can be used to decide what constitutes “optimal”
  • Predictive we want to predict the class of new samples if we know its variables
    • construct a rule or classifier
    • diagnose predictive performance (sensitivity & specificity; AUC)

What method we use depends on the size of the ’ome

  • Metabolome kinda small (~10s-100s of features)
  • Microbiome, transcriptome kinda big (~1000s of features)
  • Genome, methylome huuuge (>>1000s of features)

p versus n

  • p: number of predictors/features
    • very variable
  • n: number of samples
    • in clinical/biomedical research often ~10-100 patients or animals

p versus n

  • \(p < n\) Bayesian methods
  • \(p \approx n\) Frequentist methods
  • \(p >> n\) Deep learning methods

Platforms

Data cleaning and preprocessing

  • Remove features with all 0s or with little or no variance (they carry no information)
  • Remove samples or individuals that have too high a proportion of missing values
  • For individuals with a moderate number of missing values, there are many imputation methods

Exploratory data analysis

  • Simple methods such as PCA should be used as a first step, basically as a visualization
  • Understand structure of the data
  • Pick out any biases or errors in the data

A sampling of multiomics integration techniques

  • MOFA (multiomics factor analysis; unsupervised)
  • sPLS-DA (sparse partial least squares discriminant analysis; supervised)
  • DIABLO (sPLS-DA for multiple omics datasets)
  • Differential expression analysis
  • Machine learning-based dimension reduction/visualization methods
  • Deep learning/neural networks
  • Network analysis
  • There are more …

ML dimension reduction & visualization methods

  • PCA on steroids, uses machine learning style approach
  • Can be used to create a consensus mapping that integrates multiple omics datasets