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A comprehensive platform for analyzing longitudinal multi-omics data

Author

Listed:
  • Suhas V. Vasaikar

    (Allen Institute for Immunology)

  • Adam K. Savage

    (Allen Institute for Immunology)

  • Qiuyu Gong

    (Allen Institute for Immunology)

  • Elliott Swanson

    (Allen Institute for Immunology
    University of Washington School of Medicine)

  • Aarthi Talla

    (Allen Institute for Immunology)

  • Cara Lord

    (Allen Institute for Immunology
    GlaxoSmithKline)

  • Alexander T. Heubeck

    (Allen Institute for Immunology)

  • Julian Reading

    (Allen Institute for Immunology)

  • Lucas T. Graybuck

    (Allen Institute for Immunology)

  • Paul Meijer

    (Allen Institute for Immunology)

  • Troy R. Torgerson

    (Allen Institute for Immunology)

  • Peter J. Skene

    (Allen Institute for Immunology)

  • Thomas F. Bumol

    (Allen Institute for Immunology)

  • Xiao-jun Li

    (Allen Institute for Immunology)

Abstract

Longitudinal bulk and single-cell omics data is increasingly generated for biological and clinical research but is challenging to analyze due to its many intrinsic types of variations. We present PALMO ( https://github.com/aifimmunology/PALMO ), a platform that contains five analytical modules to examine longitudinal bulk and single-cell multi-omics data from multiple perspectives, including decomposition of sources of variations within the data, collection of stable or variable features across timepoints and participants, identification of up- or down-regulated markers across timepoints of individual participants, and investigation on samples of same participants for possible outlier events. We have tested PALMO performance on a complex longitudinal multi-omics dataset of five data modalities on the same samples and six external datasets of diverse background. Both PALMO and our longitudinal multi-omics dataset can be valuable resources to the scientific community.

Suggested Citation

  • Suhas V. Vasaikar & Adam K. Savage & Qiuyu Gong & Elliott Swanson & Aarthi Talla & Cara Lord & Alexander T. Heubeck & Julian Reading & Lucas T. Graybuck & Paul Meijer & Troy R. Torgerson & Peter J. Sk, 2023. "A comprehensive platform for analyzing longitudinal multi-omics data," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37432-w
    DOI: 10.1038/s41467-023-37432-w
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    1. Samir Rachid Zaim & Mark-Phillip Pebworth & Imran McGrath & Lauren Okada & Morgan Weiss & Julian Reading & Julie L. Czartoski & Troy R. Torgerson & M. Juliana McElrath & Thomas F. Bumol & Peter J. Ske, 2024. "MOCHA’s advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts," Nature Communications, Nature, vol. 15(1), pages 1-24, December.

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