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The tidyomics ecosystem: enhancing omic data analyses

delete2024-06-14
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PRE
AI
W
William Hutchison
T
Timothy Keyes
H
Helena L. Crowell
J
Jacques Serizay
C
Charlotte Soneson
E
Eric S. Davis
N
Noriaki Sato
L
Lambda Moses
B
Boyd Tarlinton
A
Abdullah Al Nahid
M
Miha Kosmač
Q
Quentin Clayssen
V
Victor Yuan
W
Wancen Mu
J
Ji-Eun Park
I
Izabela Mamede
M
Min Hyung Ryu
P
Pierre‐Paul Axisa
P
Paulina Paiz
C
Chi-Lam Poon
M
Ming Tang
R
Raphaël Gottardo
M
Martin Morgan
S
Stuart Lee
M
Michael Lawrence
S
Stephanie C. Hicks
G
Garry P. Nolan
K
Kara L. Davis
A
Anthony T. Papenfuss
M
Michael I. Love *
S
Stefano Mangiola *
DOI:10.1038/s41592-024-02299-2delete
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Abstract

Abstract

En 中文
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. tidyomics offers a software ecosystem for omic data manipulation and analysis that bridges Bioconductor with the tidyverse framework.

Journal

Nature Methods cover
Nature Methods
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32.1
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7.2K
Citations:
12.7W

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