Evidence map›Paper›PMID 39556595›Full record

ArticlePLoS computational biology2024

SPARTA: Interpretable functional classification of microbiomes and detection of hidden cumulative effects.

Baptiste Ruiz, Arnaud Belcour, Samuel Blanquart, Sylvie Buffet-Bataillon, Isabelle Le Huërou-Luron, Anne Siegel, Yann Le Cunff

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Baptiste RuizUniversity Rennes, Inria, CNRS, IRISA, Rennes, France.ORCID 0000-0003-2984-9334
Arnaud BelcourUniversity Rennes, Inria, CNRS, IRISA, Rennes, France.ORCID 0000-0003-1170-0785
Samuel BlanquartUniversity Rennes, Inria, CNRS, IRISA, Rennes, France.
Sylvie Buffet-BataillonInstitut NuMeCan, INRAE, INSERM, Univ Rennes, Saint-Gilles, France.
Isabelle Le Huërou-LuronInstitut NuMeCan, INRAE, INSERM, Univ Rennes, Saint-Gilles, France.
Anne SiegelUniversity Rennes, Inria, CNRS, IRISA, Rennes, France.
Yann Le CunffUniversity Rennes, Inria, CNRS, IRISA, Rennes, France.ORCID 0000-0002-3068-5675

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The composition of the gut microbiota is a known factor in various diseases and has proven to be a strong basis for automatic classification of disease state. A need for a better understanding of microbiota data on the functional scale has since been voiced, as it would enhance these approaches' biological interpretability. In this paper, we have developed a computational pipeline for integrating the functional annotation of the gut microbiota into an automatic classification process and facilitating downstream interpretation of its results. The process takes as input taxonomic composition data, which can be built from 16S or whole genome sequencing, and links each component to its functional annotations through interrogation of the UniProt database. A functional profile of the gut microbiota is built from this basis. Both profiles, microbial and functional, are used to train Random Forest classifiers to discern unhealthy from control samples. SPARTA ensures full reproducibility and exploration of inherent variability by extending state-of-the-art methods in three dimensions: increased number of trained random forests, selection of important variables with an iterative process, repetition of full selection process from different seeds. This process shows that the translation of the microbiota into functional profiles gives non-significantly different performances when compared to microbial profiles on 5 of 6 datasets. This approach's main contribution however stems from its interpretability rather than its performance: through repetition, it also outputs a robust subset of discriminant variables. These selections were shown to be more consistent than those obtained by a state-of-the-art method, and their contents were validated through a manual bibliographic research. The interconnections between selected taxa and functional annotations were also analyzed and revealed that important annotations emerge from the cumulated influence of non-selected taxa.

Indexed as

Computational BiologyGastrointestinal MicrobiomeAlgorithmsBacteriaHumansMicrobiotaReproducibility of ResultsRNA, Ribosomal, 16SSoftwareRNA, Ribosomal, 16S

Identifiers

PMID39556595
PMCPMC11611268

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.