The tidyomics ecosystem: enhancing omic data analyses

Hutchison, WJ; Keyes, TJ; Crowell, HL; Serizay, J; Soneson, C; Davis, ES; Sato, N; Moses, L; Tarlinton, B; Nahid, AA; Kosmac, M; Clayssen, Q; Yuan, V; Mu, W; Park, JE; Mamede, I; Ryu, MH; Axisa, PP; Paiz, P; Poon, CL; Tang, M; Gottardo, R; Morgan, M; Lee, S; Lawrence, M; Hicks, SC; Nolan, GP; Davis, KL; Papenfuss, AT; Love, MI; Mangiola, S

Author(s): Hutchison, WJ; Keyes, TJ; Crowell, HL; Serizay, J; Soneson, C; Davis, ES; Sato, N; Moses, L; Tarlinton, B; Nahid, AA; Kosmac, M; Clayssen, Q; Yuan, V; Mu, W; Park, JE; Mamede, I; Ryu, MH; Axisa, PP; Paiz, P; Poon, CL; Tang, M; Gottardo, R; Morgan, M; Lee, S; Lawrence, M; Hicks, SC; Nolan, GP; Davis, KL; Papenfuss, AT; Love, MI; Mangiola, S;
Details: Publication Year 2024-06-14,Volume 21,Issue #7,Page 1166-1170
Journal Title: Nature Methods
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.
Publisher: Springer Nature
Keywords: Humans; *Software; Computational Biology/methods; Leukocytes, Mononuclear/metabolism/cytology; Genomics/methods; Data Analysis
Research Division(s): Bioinformatics
PubMed ID: 38877315
Publisher's Version: https://doi.org/10.1038/s41592-024-02299-2
Terms of Use/Rights Notice: Refer to copyright notice on published article.

Creation Date: 2024-07-26 09:30:27

Last Modified: 2024-07-26 09:43:09