{
  "abstract": "Patient cohort profiling increasingly includes structured views for multiple modalities, such as single-cell RNA sequencing, spatial transcriptomics or proteomics, and histology, each providing multiple subobservations per patient, including single cells, spatial spots or patches. To model such data along with simple patient-level views, current multimodal integration methods typically rely on separately precomputed summaries and fail to fully leverage information in structured views. Here we present FACTMx, a variational framework that jointly models structured and simple views to learn interpretable patient-level representations. FACTMx couples latent patient factors with subobservation clustering and per-patient component proportions, enabling direct interpretation and downstream association analyses. The framework supports different structured-view mixture assumptions, including topic- and Gaussian-structured data, while retaining modular encoder-decoder parameterisations. In simulations spanning sparse and dense dependencies and multiple noise regimes, FACTMx improved reconstruction, integration and recovery of structured components relative to previous methods. Applied to non-small cell lung cancer cohorts, FACTMx captured survival-associated latent signals linked to immune microenvironments, gene expression pathways and spatially coherent histological patterns. In a longitudinal coronary syndrome cohort, FACTMx highlighted an outcome-associated axis connected to ejection-fraction change, immune cell states, soluble mediators and cardiac injury markers. These results support joint structured-simple modelling for interpretable multimodal patient stratification.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland; Institute of AI for Health, Helmholtz Munich, Neuherberg, Germany"
  ],
  "article_type": "Confirmatory results",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "name": "K. Oksza-Orzechowski"
    },
    {
      "name": "M. Lazecka"
    },
    {
      "name": "L. Koperski"
    },
    {
      "name": "D. Wojtowicz"
    },
    {
      "name": "M. Mozejko"
    },
    {
      "name": "D. Schulz"
    },
    {
      "name": "R. Liechti"
    },
    {
      "name": "F. Marzetta"
    },
    {
      "name": "M. Morfouace"
    },
    {
      "name": "H. S. Hong"
    },
    {
      "name": "S. Tissot"
    },
    {
      "name": "B. Bodenmiller"
    },
    {
      "name": "E. Staub"
    },
    {
      "affs": [
        0
      ],
      "name": "E. Szczurek"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/44bzwb665x3z1.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.31.748244",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748244v1.full",
  "id": "44bzwb665x3z1",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748244v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7208747540",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748244v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-31",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/44bzwb665x3z1.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:23Z",
      "native_id": "10.64898/2026.08.31.748244",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748244v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Modelling interpretable patient-level representationsfrom structured and simple multimodal data",
  "title_text": "Modelling interpretable patient-level representationsfrom structured and simple multimodal data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:23Z",
  "url": "https://research.draconitic.com/paper/44bzwb665x3z1",
  "venue": "bioRxiv",
  "versions": []
}