The Tidyomics Ecosystem: Enhancing Omic Data Analyses

Overview

Journal Nat Methods

Specialties Biomedical Engineering
Pathology

Date 2024 Jun 14

PMID 38877315

Authors

William J Hutchison

Timothy J Keyes

Helena L Crowell

Jacques Serizay

Charlotte Soneson

Eric S Davis

Noriaki Sato

Lambda Moses

Boyd Tarlinton

Abdullah A Nahid

Miha Kosmac

Quentin Clayssen

Victor Yuan

Wancen Mu

Ji-Eun Park

Izabela Mamede

Min Hyung Ryu

Pierre-Paul Axisa

Paulina Paiz

Chi-Lam Poon

Ming Tang

Raphael Gottardo

Martin Morgan

Stuart Lee

Michael Lawrence

Stephanie C Hicks

Garry P Nolan

Kara L Davis

Anthony T Papenfuss

Michael I Love

Stefano Mangiola

Affiliations

Soon will be listed here.

Abstract

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

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