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TuomasBorman
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Mar 24, 2026
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| # Computational environments {#sec-computational_environments} | ||
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| Several computational environments are available for microbiome data science, | ||
| each distinguished by its underlying paradigms, user communities, and scope of | ||
| functionality. For example, tools such as QIIME 2 [@bolyen2019], Anvi'o | ||
| [@eren2021], and Mothur [@schloss2009] emphasize standardized, workflow-oriented | ||
| approaches tailored to microbiome analysis. While these platforms provide robust | ||
| and well-validated solutions for microbiome data, they are typically designed | ||
| around specific data types and predefined pipelines, which can limit flexibility | ||
| when integrating heterogeneous data modalities within a unified analytical | ||
| framework. In addition, their workflow-centric design may not fully accommodate | ||
| the iterative nature of exploratory microbiome research. | ||
| Python-based ecosystems built around libraries such as Biopython [@cock2009] | ||
| and scikit-bio [@rideout2023] provide flexible, general-purpose toolkits, but | ||
| are less comprehensive than Bioconductor. As a result, users are often required | ||
| to implement more custom scripting and assemble their own analytical workflows. | ||
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| Bioconductor provides a comprehensive ecosystem of tools spanning multiple areas | ||
| of bioinformatics, extending beyond microbiome research. It facilitates | ||
| interoperability across domains by leveraging a shared data infrastructure, | ||
| enabling more seamless integration of multi-omics data. Rather than focusing on | ||
| predefined workflows, Bioconductor is oriented towards exploratory and | ||
| statistical data analysis, with components that can be flexibly combined into | ||
| custom workflows. While Python-based ecosystems benefit from rapidly evolving | ||
| machine learning and artificial intelligence tools, Bioconductor’s strengths lie | ||
| in robust statistical modeling and strong interoperability with other | ||
| computational environments. In addition, it is supported by a large, active | ||
| community and a mature, well-curated package ecosystem. Although this breadth | ||
| and complexity can introduce challenges, such as dependency management and | ||
| package compatibility, these are actively addressed through community standards, | ||
| coordinated release cycles, and continuous integration practices. | ||
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The reviewer stated that we do not compare Bioconductor to other ecosystems enough. They felt that we imply Bioconductor is superior to others without providing justification.
TuomasBorman
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Mar 24, 2026
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| isolation. However, this growing complexity introduces additional overhead, as | ||
| researchers must track samples and features across multiple tables and manage an | ||
| increasing number of data elements. Moreover, input data formats vary across | ||
| different methods, leading to technical complications and time lost on data | ||
| wrangling. Bioconductor addresses these challenges through standardized data | ||
| structures supported by an ecosystem of interoperable methods, allowing the | ||
| analyst to invest more time in core analytical tasks. | ||
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This aims to clarify more the idea of data containers
TuomasBorman
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Mar 24, 2026
| yet flexible workflows. In particular, the direct interoperability with the | ||
| yet flexible workflows, as TreeSE inherits full compatibility with methods | ||
| designed for SE. In particular, the direct interoperability with the | ||
| widely adopted SE data science ecosystem distinguishes our TreeSE-based |
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This highlights that all methods that are using SE can be used with TreeSE.
We could consider highlighting SummarizedExperiment more (instead of TreeSE, we could talk about SE). It would emphasize that this framework is integrated to SE ecosystem
TuomasBorman
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Mar 24, 2026
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| [@lawrence2013]. Linking data across modalities presents an additional technical | ||
| challenge, as multi-omics data are often sparse and only partially matched | ||
| across experiments. For example, one study may include 100 samples with | ||
| metagenomics sequencing data but only 40 with matched metabolomics. In other | ||
| cases, a single sample in one modality may link to two or more samples in | ||
| another modality (*e.g.*, due to technical or spatial replication) | ||
| [see @husso2023]. These scenarios require flexible sample mappings and the | ||
| ability to accommodate heterogeneous data containers across modalities. | ||
| The MultiAssayExperiment (MAE) data container [@ramos2017] addresses these | ||
| challenges by introducing a formal mapping layer (sampleMap) that links | ||
| biological samples to their corresponding measurements in each assay | ||
| (@fig-datacontainers b). This design supports one-to-one, one-to-many, and | ||
| partially overlapping sample relationships across experiments. Importantly, the | ||
| mapping is used during downstream operations: when subsetting an MAE object | ||
| (*e.g.*, selecting samples with both microbiome and metabolome data), the | ||
| framework automatically resolves the intersection of available samples and | ||
| ensures consistent alignment across all assays. Such structured handling of | ||
| sample relationships is particularly important for integrative methods, as it | ||
| reduces the risk of sample mismatches, a common source of irreproducibility in | ||
| ad hoc multi-table analyses. For instance, diagonal integration methods |
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This aims to clarify how MultiAssayExperiment handles the mapping and benefits of using it
TuomasBorman
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Mar 24, 2026
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| omics data sets [@huber2015; @ramos2017; @amezquita2020]. By leveraging | ||
| standardized multi-assay data structures, users can apply a growing | ||
| number of integrative methods for multi-omics analysis. This eliminates the | ||
| need for manual data wrangling, reduces the risk of errors (*e.g.*, sample | ||
| mismatches), and enables the creation of modular, efficient, and reproducible | ||
| workflows. Such integrative approaches in microbiome |
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This highlights more the benefits of MultiAssayExperiment
TuomasBorman
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Mar 24, 2026
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| @barker2022; @hocquet2024]. Harmonized data structures and access to validated | ||
| methods help shape such best practices by enabling transparent benchmarking and | ||
| fostering community-driven consensus, while allowing users to flexibly select | ||
| and evaluate methods appropriate for their specific data and research questions. | ||
| Community formation is actively supported through | ||
| online training resources and communication channels. Together with the | ||
| interactive applications, these resources provide accessible entry points for |
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Reviewer stated that we could add more on best practices. We cannot list them as they are evolving and depends on research questions etc. But we can clarify how the framework relates to best practices
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