The<i>tidyomics</i>ecosystem: Enhancing omic data analyses

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

doi:10.1101/2023.09.10.557072

Cited by 4 publications

(4 citation statements)

References 41 publications

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“…Data wrangling was done through tidyverse 101 . Single-cell data analysis and manipulation were done through Seurat 95 (version 4.0.1), tidyseurat 104 (version 0.3.0), tidySingleCellExperiment 96 (version 1.9.4), and tidybulk 20 (version 1.6.1). Colour-blind-friendly colouring was aided by dittoseq 106 .…”

Section: Methodsmentioning

confidence: 99%

A multi-organ map of the human immune system across age, sex and ethnicity

Mangiola¹,

Milton²,

Ranathunga³

et al. 2023

Preprint

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The presence of immune cells in non-lymphoid tissues throughout the body is vital in the fight against infections and cancer. Yet, a detailed map of immune cell distribution and interactions throughout the body is lacking. To address this gap, we harmonised and annotated 29 million cells across 12,981 single-cell RNA sequencing samples covering 45 anatomical sites to create a comprehensive compositional and communication map of the healthy immune system. This resource represents a 30-fold increase in cell count and a 10-fold increase in tissue coverage compared to the existing state-of-the-art. We used this resource to model compositional changes in the immune system with age and diversity across demographic groups and to investigate the impact of different technologies using a novel multilevel Bayesian model. Our analysis revealed patterns of progressive tissue-specific inflammation, loss of plasticity with age, and significant differences between ethnicities. This study represents a comprehensive healthy reference for precision medicine, immunology, infectious disease, and cancer.

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Section: Methodsmentioning

confidence: 99%

A multi-organ map of the human immune system across age, sex and ethnicity

Mangiola¹,

Milton²,

Ranathunga³

et al. 2023

Preprint

Self Cite

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“…Tidy analysis of omics data has recently gained traction in large communities of bioinformaticians and programming languages (Hutchison et al, 2023), and tidyCoverage fully adheres to the tidy data paradigm. The package supports operative verbs defined in the tidyverse, such as filter, mutate, group_by or expand for CoverageExperiment and AggregatedCoverage objects.…”

Section: Tidy Principles For Epigenomicsmentioning

confidence: 99%

“…genomation (Akalin et al, 2015), ATACseqQC (Ou et al, 2018) or soGGI (Dharmalingam, n.d.). However, these softwares (1) are not interconnected to existing bioinformatic resources, (2) do not efficiently leverage the Bioconductor ecosystem and (3) do not use a tidy, intuitive syntax for data processing (Hutchison et al, 2023; Wickham et al, 2019). Here, we present tidyCoverage , an R package extending Bioconductor fundamental data structures and reusing principles of tidy data manipulation to extract and aggregate coverage tracks over multiple sets of genomic features.…”

Section: Introductionmentioning

confidence: 99%

Epigenomics coverage data extraction and aggregation in R with tidyCoverage

Serizay,

Koszul

2024

Preprint

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The tidyCoverage R package provides a framework for intuitive investigation of collections of genomic tracks over genomic features, relying on the principle of tidy data manipulation. It defines two data structures, CoverageExperiment and AggregatedCoverage classes, directly extending the SummarizedExperiment fundamental class, and introduces a principled approach to exploring genome-wide data. This infrastructure facilitates the extraction and manipulation of genomic coverage track data across individual or multiple sets of thousands of genomic loci. This allows the end user to rapidly visualize track coverage at individual genomic loci or aggregated coverage profiles over sets of genomic loci. tidyCoverage seamlessly combines with the existing Bioconductor ecosystem to accelerate the integration of genome-wide track data in epigenomic analysis workflows. tidyCoverage emerges as a valuable tool, contributing to the advancement of epigenomics research by promoting consistency, reproducibility, and accessibility in data analysis.

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“…Its appeal stems from its innate simplicity and efficacy in organizing and processing data 10 . In recent times, a plethora of tools have been devised to address distinct omics data processing and analysis needs, including notable initiatives such as the tidymass project 11 , tidyomics project 12 , tidymicro 13 , and MicrobiotaProcess 13,14 . However, a conspicuous gap persists in the form of a standardized, tidyverse-based package for seamless and rigorous microbiome data processing and analysis.…”

Section: Introductionmentioning

confidence: 99%

microbiomedataset: A tidyverse-style framework for organizing and processing microbiome data

Shen,

Snyder

2023

Preprint

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Microbial communities exert a substantial influence on human health and have been unequivocally associated with a spectrum of human maladies, encompassing conditions such as anxiety1, depression2, hypertension3, cardiovascular diseases4, obesity4,5, diabetes6, inflammatory bowel disease7, and cancer8,9. This intricate interplay between microbiota community structures and host pathophysiology has kindled substantial interest and spurred active research endeavors across various scientific domains. Despite significant strides in sequencing technologies, which have unveiled the vast diversity of microbial populations across diverse ecosystems, the analysis of microbiome data remains a formidable challenge. The complexity inherent in such data, compounded by the absence of standardized data processing and analysis workflows, continues to pose substantial hurdles. The tidyverse paradigm, comprised of a suite of R packages meticulously crafted to facilitate efficient data manipulation and visualization, has garnered considerable acclaim within the data science community10. Its appeal stems from its innate simplicity and efficacy in organizing and processing data10. In recent times, a plethora of tools have been devised to address distinct omics data processing and analysis needs, including notable initiatives such as the tidymass project11, tidyomics project12, tidymicro13, and MicrobiotaProcess13,14. However, a conspicuous gap persists in the form of a standardized, tidyverse-based package for seamless and rigorous microbiome data processing and analysis. To address this burgeoning demand for standardized and reproducible microbiome data analysis, we introduce microbiomedataset, an R package that embraces the tidyverse ethos to furnish a structured framework for the organization and processing of microbiome data. Microbiomedataset offers a comprehensive, customizable solution for the management, structuring, and processing of microbiome data. Importantly, this package seamlessly integrates with established bioinformatics tools, facilitating its incorporation into existing analytical pipelines11,13,14,15. Within this manuscript, we proffer an in-depth overview of the microbiomedataset package, elucidating its multifarious functionalities. Moreover, we substantiate its utility through illustrative case studies employing a publicly available microbiome dataset. It is imperative to underscore that microbiomedataset constitutes an integral component of the larger tidymicrobiome project, accessible via www.tidymicrobiome.org. Tidymicrobiome epitomizes an ecosystem of R packages that share a coherent design philosophy, grammar, and data structure, collectively engendering a robust, reproducible, and object-oriented computational framework. This project's development has been guided by several key tenets: (1) Cross-platform compatibility, (2) Uniformity, shareability, traceability, and reproducibility, and (3) Flexibility and extensibility. We further expound upon the advantages inherent in adopting a tidyverse-style framework for microbiome data analysis, underscoring the pronounced benefits in terms of standardization and reproducibility that microbiomedataset offers. In sum, microbiomedataset furnishes an accessible and efficient avenue for microbiome data analysis, catering to both neophyte and seasoned R users alike.

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Thetidyomicsecosystem: Enhancing omic data analyses

Cited by 4 publications

References 41 publications

A multi-organ map of the human immune system across age, sex and ethnicity

A multi-organ map of the human immune system across age, sex and ethnicity

Epigenomics coverage data extraction and aggregation in R with tidyCoverage

microbiomedataset: A tidyverse-style framework for organizing and processing microbiome data

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