Multi-omic integration for enhanced interpretability in exploratory analyses

The increased availability and affordability of high-throughput sequencing technologies in recent years has facilitated the use of multi-omic studies to expand and enrich our understanding of complex systems across hierarchical biological levels. Integrative methods for these heterogeneous and multi-faceted ‘omics data have shown promise for enhancing the interpretability of exploratory analyses, improving predictive power, and contributing to a holistic understanding of systems biology. However, such integrative analyses are accompanied by several major obstacles, including the unknown hierarchy and potentially ambiguous relationships among different sources of data, high dimensionality coupled with small sample sizes, issues due to batch effects and quality control, potentially incomplete or missing data… and the occasional difficulty in posing well-defined and answerable research questions of such data. In light of these challenges, in this talk I will discuss two recent methodological contributions to exploratory integrative multi-omic analyses: (1) padma, a multiple factor analysis approach for quantifying and visualizing individualized multi-omic pathway deviation patterns; and (2) maskmeans, an approach for aggregating/splitting an existing clustering partition using multi-view data. Finally, I will discuss some practical considerations for multi-omics integration in practice, as well as some current and future areas of methodological research in this area.

Transcript

1. Multi-omic integration for enhanced
   interpretability in exploratory analyses
   ANDREA RAU
   LABORATOIRE JEAN KUNTZMANN SEMINAR
   @ZOOM
   APRIL 29, 2021
   1
   https://andrea-rau.com @andreamrau slides: https://tinyurl.com/Grenoble2021-Rau
   

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2. 2
   Gene
   expression
   TTTGCA
   AAACGT
   TF
   Transcription
   factor
   expression
   Copy number alterations
   The multi-omics data landscape
   Promoter methylation
   microRNA
   expression
   …GCAGCGTTCGA…
   …GCAACGTTAGA…
   Somatic mutations
   Germline genetic variation
   Enhancer
   Accessibility
   Protein
   abundance
   Metabolite
   concentrations
   … + Histone modifications + RNA processing/stability + 3D conformation + Microbiome composition + …
   

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3. 3
   - Comprehensive, multi-dimensional maps of key genomic changes in 33 cancer types
   from n = 11k+ individuals
   ◦ RNA-seq, miRNA-seq, copy number alterations, methylation, somatic mutations, protein abundance,
   genotypes, histological data, clinical data → p ~ 100s to 1000s to 100k+
   - Publically available data (multi-tiered data depending on patient identifiability)
   - Widely used by the research community (1000+ publications by TCGA network +
   independent researchers)
   Large-scale (public) matched multi-omics
   The Cancer Genome Atlas (TCGA)
   Image: Corces et al. (2018)
   

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4. 4
   No
   regenerative
   response =
   disability
   Robust
   regenerative
   response =
   functional
   recovery
   Gene expression + Chromatin accessibility
   (RNA-seq + ATAC-seq)
   Dhara et al. (2019) Scientific Reports; Rau et al. (2019) G3 4
   Smaller-scale matched multi-omics
   Central nervous system injury in zebrafish
   Regulatory network involved in
   CNS rewiring during optic nerve
   regeneration in zebrafish
   n = 15
   (5 times ×
   3 reps)
   p ~ 20k
   

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5. 5
   Even smaller-scale matched multi-omics
   Functional annotation of livestock genomes
   Foissac et al. (2019)
   

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6. 6
   - Many more biological entities than individuals (p >> n)
   - Experimental design
   - Normalization / standardization / pre-processing, potentially
   heterogenous quality across datasets, substantial batch effects
   - Missing or incomplete data (e.g., MI-MFA1)
   - Look-everywhere effect
   Some challenges of multi-omic data analysis
   https://bioinformatics.mdanderson.org/BatchEffectsViewer/
   1 Voillet et al. 2016; 2Ramos et al. (2017), https://bioconductor.org/packages/MultiAssayExperiment/
   MultiAssayExperiment:
   coordinated representation
   + storage + analysis of
   multi-omics data2
   

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7. 7
   - Horizontal versus vertical integration
   - Account for (known/unknown) interdependencies
   within and across data types
   - (Partially) matched omics data across samples or
   biological entities (e.g., genes)
   - In some contexts, limited/incomplete a priori
   knowledge of relevant phenotype groups for
   comparisons = unsupervised analysis
   Multi-omic data → Multivariate, multi-table methods
   Multi-{domain, way, view, modal, table, omics} data
   How do we integrate multi-omic data?
   What question are we specifically addressing?
   How can we use multi-omic data to answer that question?
   Image: Rajasundaram and Selbig (2016)
   

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8. 8
   Broad umbrella of integrative data analysis
   Many different answers, depending on the question…
   Exploration / description
   • Find underlying relationships between datasets
   • Clustering, unsupervised classification
   Prediction
   • Identify small set of features (i.e., biomarkers) that yields best possible
   prediction
   • Remove noisy or redundant feature, curse of dimensionality
   • Use set of features to understand the underlying biology
   Causality
   • Extract mechanistic hypotheses and insights
   http://factominer.free.fr, http://mixomics.org/
   

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9. 9
   For a given pathway of interest, can we identify and
   quantify highly aberrant individuals in a sample based
   on multi-omic data?
   Does patient prognosis correlate with large pathway deviation scores?
   Which individuals have the most aberrant profiles for pathways of interest?
   Which genes / omic drive these aberrant scores?
   Integrative multi-omics methods: Multivariate analysis
   

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10. A
    B
    C
    Individuals
    1 / λA
    1 / λB
    1 / λC
    Individuals
    1 / λA
    1 / λB
    1 / λC
    PC 1
    PC 2
    !
    10
    Define an individualized pathway-level deviation score based
    on multi-omic data using MFA
    http://github.com/andreamrau/padma Rau et al. (2020) Biostatistics, https://doi.org/10.1101/827022
    padma: Pathway deviation scores using Multiple Factor Analysis
    i
    

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11. 11
    Applying padma to TCGA multi-omics data
    Breast invasive carcinoma (BRCA; n = 504) and lung adenocarcinoma (LUAD; n = 144)
    • Batch correction performed using removeBatchEffects in limma
    • RNA-seq + promoter methylation + copy number alterations + miRNA-seq
    • miRNA → gene mapping provided by miRTarBase (exact matches, Functional MTI predictions)
    • 1136 MSigDB curated canonical pathways (Biocarta, PID, Reactome, Sigma Aldrich, Signaling Gateway,
    Signal Transduction Knowledge Environment, Matrisome Project)
    Patient prognosis measured using progression-free interval survival times (LUAD) and
    histological grade (BRCA)
    Rau et al. (2020) Biostatistics
    

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12. Which individuals have the most highly aberrant multi-omic profiles?
    12
    D4-GDP dissociation inhibitor signaling pathway, LUAD (Cox PH*, BH padj = 0.0111)
    Rau et al. (2020) Biostatistics
    

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13. Which genes/omics drive large pathway deviation scores?
    13
    → CASP1, CASP3, and
    CASP8 all have high
    gene-level deviation
    scores for the two most
    extreme individuals…
    Rau et al. (2020) Biostatistics
    

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14. Which genes/omics drive large pathway deviation scores?
    14
    Rau et al. (2020) Biostatistics
    

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15. 15
    • Larger padma deviation scores = increasingly aberrant pathway variation with significantly worse prognosis
    (survival, histological grade) in breast and lung cancer
    • Potential outlier detection tool
    Innovative use of existing MFA method to
    calculate and graphically explore
    individualized multi-omic pathway deviation scores
    Next steps…
    • Incorporation of known hierarchical structure among genes in pathway
    • Extensions for highly structured data (e.g., multi-omic data from divergent chicken lines subject to feed/heat stress
    or maize diversity panels under control/cold conditions)
    padma results on TCGA breast and lung cancer
    (RNA-seq + miRNA-seq + methylation + CNA data, MSigDB canonical pathways)
    Rau et al. (2020) Biostatistics
    

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16. 16
    Integrative multi-omics methods: Clustering
    Clustering individuals based on single omics (especially gene expression) data widely used to
    identify molecular subtypes of cancer
    • PAM50, AIMS intrinsic subtypes
    • Many methods have been developed
    Recently, many integrative clustering methods have proposed to make use of multi-omic data
    • Rich literature in machine learning on multi-view methods
    • Multi-omic specific methods: MVDA, iCluster+, MOFA, …
    • Primarily de novo clustering from multi-omics data
    How can an existing clustering be merged or split based on multi-omics data?
    e.g., subdivide intrinsic subtypes into distinct sub-groups of individuals
    

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17. 17
    maskmeans: Multi-view aggregation/splitting K-means
    𝑍 = (𝑍 1 , … , 𝑍 𝑣 , …, 𝑍 𝑉 )
    where each 𝑍 𝑣 is scaled to unit-variance and additionally divided by the size of its view:
    𝑋 𝑣 = 𝑍 𝑣 /𝑑𝑣
    Aggregation/splitting of initial clustering of the n individuals based on the minimization of a
    criterion similar to the multi-view fuzzy K-means algorithm* with tuning parameters 𝛾, 𝛿 > 1:
    * Wang and Chen (2017); Godichon-Baggioni et al. (2020) AOAS; http://github.com/andreamrau/maskmeans
    ෍
    𝑖=1
    𝑛
    ෍
    𝑘=1
    𝐾
    ෍
    𝑣=1
    𝑉
    (𝛼𝑘,𝑣
    )𝛾(𝜋𝑖,𝑘
    )𝛿 𝑋
    𝑖
    (𝑣) − 𝜇
    𝑘
    (𝑣) 2
    Clustering
    partition
    Per-view
    cluster centers
    Per-cluster,
    per-view weights
    

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18. 18
    Multi-view splitting K-means algorithm
    Godichon-Baggioni et al. (2020) AOAS
    

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19. 19
    Multi-view splitting/aggregating K-means algorithm: Simulations
    Godichon-Baggioni et al. (2020) AOAS
    • K = 7 clusters
    • n = 100
    • V = 6 views
    Split: Kinit
    = 4 from
    View 2 data
    Aggregate: Kinit
    = 20
    fromView 1 data
    True labels from
    View 1
    → 100 simulated
    datasets
    

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20. 20
    Multi-view splitting/aggregating K-means algorithm: Simulations
    Godichon-Baggioni et al. (2020) AOAS
    

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21. 21
    Multi-view splitting/aggregating K-means algorithm: Simulations
    Godichon-Baggioni et al. (2020) AOAS
    

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22. n = 61 n = 38 n = 228 n = 136 n = 43
    22
    maskmeans for TCGA breast cancer
    n = 506 patients; focus on subset of 226 genes (TP53, MKI67, estrogen signaling and ErbB signaling pathways, and the SAM40
    DNA methylation signature) and 149 miRNAs with avg normalized expression > 50
    Godichon-Baggioni et al. (2020) AOAS
    Age at diagnosis + menopause status
    Number of lymph nodes
    

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23. Some final remarks on multi-omics
    …and answering questions that we have not yet thought to ask1
    Multi-omic data integration often requires a combination of software tools +
    technical expertise + domain expertise…
    Utility of tools for rapid querying + (interactive) exploration of fully processed
    data without advanced coding knowledge
    Reproducibility
    Communication + vocabulary is key!
    Emergence of single-cell and time-course multi-omics data
    Dealing with partially matched data, transfer learning strategies, …
    1 Stein-O’Brien et al. (2018) Trends in Genetics
    Matrix factorization?
    Decomposition?
    Latent factor model? ...
    

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24. 24
    In progress: multi-omics and genomic prediction
    PhD work of Fanny Mollandin (H2020 GENE-SWitCH)
    Goal: accurate phenotype prediction + interpretability
    

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25. 25
    In progress: multi-omics and genomic prediction
    PhD work of Fanny Mollandin (H2020 GENE-SWitCH)
    

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26. Acknowledgements
    26
    26
    https://andrea-rau.com @andreamrau slides: https://tinyurl.com/Grenoble2021-Rau
    

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