The tidyomics ecosystem: Enhancing omic data analyses

• Published in: bioRxiv
• Main Authors: Hutchison, William J, Keyes, Timothy J, Crowell, Helena L, Serizay, Jacques, Soneson, Charlotte, Davis, Eric S, Sato, Noriaki, Moses, Lambda, Tarlinton, Boyd, Nahid, Abdullah A, Kosmac, Miha, Clayssen, Quentin, Yuan, Victor, Mu, Wancen, Park, Ji-Eun, Mamede, Izabela, Ryu, Min Hyung, Axisa, Pierre-Paul, Paiz, Paulina, Poon, Chi-Lam, Tang, Ming, Gottardo, Raphael, Morgan, Martin, Lee, Stuart, Lawrence, Michael, Hicks, Stephanie C, Nolan, Garry P, Davis, Kara L, Papenfuss, Anthony T, Love, Michael I, Mangiola, Stefano
• Format: Journal Article Paper
• Language: English
• Published: United States Cold Spring Harbor Laboratory 22.05.2024
• Edition: 1.3
• Subjects: Bioinformatics
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2023.09.10.557072

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 standard for data organisation and manipulation. Here, we present the 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 by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas , spanning six data frameworks and ten analysis tools.