phenomenon · claim-type · cross-domain-transfer · mechanism · claim-relation · replication-status · limitation — grounded in the full text| phenomenon | differential abundance of microbial features between phenotypes— “This problem is referred to as class comparison: how can the differences between phenotypes such as tumor subtype or disease state be explained in terms of consistent biological pathways or molecular mechanisms?” | |
| claim-type | method— “LEfSe determines the features (organisms, clades, operational taxonomic units, genes, or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance.” | |
| method — evidence | — “Specifically, we first use the non-parametric factorial Kruskal- Wallis (KW) sum-rank test [49] to detect features with significant differential abundance with respect to the class of interest; biological consistency is subsequently investigated using a set of pairwise tests among subclasses using the (unpaired) Wilcoxon rank-sum test… | |
| object of study — evidence | — “LEfSe determines the features (organisms, clades, operational taxonomic units, genes, or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance.” | |
| research question — evidence | — “Specifically, LEfSe determines features potentially able to explain the differences among conditions rather than the features that simply possess uneven distributions among classes.” | |
| contribution — evidence | — “However, none of these methods, even those offering nuanced analyses of metagenomic data, provide biological class explanations to establish statistical significance, biological consistency, and effect size estimation of predicted biomarkers.” | |
| mechanism | THREE TESTS IN SEQUENCE, EACH ASKING A DIFFERENT QUESTION: a Kruskal-Wallis rank test for whether the feature differs at all, pairwise Wilcoxon tests among SUBCLASSES for whether the difference is consistent with the population structure, and LDA for how big it is— “Specifically, we first use the non-parametric factorial Kruskal- Wallis (KW) sum-rank test [49] to detect features with significant differential abundance with respect to the class of interest; biological consistency is subsequently investigated using a set of pairwise tests among subclasses using the (unpaired) Wilcoxon rank-sum test… | mechanisms[0] |
| mechanism | EXPLAINING RATHER THAN DETECTING: the method keeps features able to explain the difference between conditions, not merely those with uneven distributions - which is what a significance test alone returns— “Specifically, LEfSe determines features potentially able to explain the differences among conditions rather than the features that simply possess uneven distributions among classes.” | mechanisms[1] |
| mechanism | PRIOR KNOWLEDGE AS A CONSTRAINT ON A HIGH-DIMENSIONAL SEARCH: the biological hypothesis is encoded and used to narrow the space, which is the paper's answer to having far more candidate biomarkers than samples— “The introduction of prior biological knowledge in the method contributes to constrain the analysis and thus to address the challenges traditionally connected with high-dimensional data mining.” | mechanisms[2] |
| sense | class comparison - EXPLAINING a difference between phenotypes in terms of consistent pathways or mechanisms. Distinguished by the paper from class DISCOVERY (finding subtypes) and class PREDICTION (assigning a new sample) - three tasks, one dataset, and the paper says which one it is doing— “This problem is referred to as class comparison: how can the differences between phenotypes such as tumor subtype or disease state be explained in terms of consistent biological pathways or molecular mechanisms?” | senses[0] |
| sense | effect size - USED HERE AS AN ORTHOGONAL AXIS TO THE P-VALUE, not as a refinement of it: a very significant difference can be too small to matter, and a marker at p = 0.01 can carry a large effect whose significance was eaten by technical noise— “Differences between classes can be very statistically significant (low P-value) but so small that they are unlikely to be biologically responsible for phenotypic differences.” | senses[1] |
| control | the approach validated on three independent bodies of data - human microbiomes, a mouse model of ulcerative colitis, and environmental samples — held — rules out a biomarker method tuned to one dataset's structure - three data sources of different kinds are what shows the discovery is not a property of any one of them— “We validated this approach using data from human microbiomes, a mouse model of ulcerative colitis, and environmental samples, in each case predicting groups of organisms or operational taxonomic units that concisely differentiate the classes being compared.” | controls[0] |
| edge | class comparison —distinct-from→ class prediction— “Note that as we are performing class comparison rather than class prediction, it is worth specifying that the effect size estimation accuracy of an algorithm is not directly connected with its predictive ability” | concept_edges[0] |
| edge | effect size —distinct-from→ statistical significance— “On the other hand, a biomarker with a relatively large P-value (for example, 0.01) may correspond to a large effect size, with statistical significance diminished by technical noise.” | concept_edges[1] |
| edge | biological consistency —part-of→ LEfSe— “LEfSe determines the features (organisms, clades, operational taxonomic units, genes, or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance.” | concept_edges[2] |
| edge | effect relevance —part-of→ LEfSe— “LEfSe determines the features (organisms, clades, operational taxonomic units, genes, or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance.” | concept_edges[3] |
| outcome (stated by the tagger, no quotation) | control — held | controls[0] |
| evidence status note | limitation — the quote carries a spurious space inside a word (taxonomy shape 8): “associated with” is staged as “associated w ith”. Confidence 0.85.— “Related challenges are associated w ith high-dimensional data regardless of the data type or experimental platform; the number of potential biomarkers, for example, is typically much higher than the number of samples” | limitations[1] |
| evidence status note | mechanism — the quote carries a line-break hyphen scar: the paper's “Kruskal-Wallis” is staged as “Kruskal- Wallis”. Verbatim against the staged text; the damage is mid-name and cannot be re-sliced around. Confidence 0.85 under an earlier note of ours.— “Specifically, we first use the non-parametric factorial Kruskal- Wallis (KW) sum-rank test [49] to detect features with significant differential abundance with respect to the class of interest; biological consistency is subsequently investigated using a set of pairwise tests among subclasses using the (unpaired) Wilcoxon rank-sum test… | mechanisms[0] |
| limitation | THE TRADE IS STATED AND PRICED: a substantially better false positive rate is bought with a moderately increased false negative rate, and the paper says the user can adjust where it sits— “We further evaluated LEfSe using synthetic data, observing that it achieves a substantially better false positive rate compared to standard statistical tests, at the price of a moderately increased false negative rate (that can be adjusted as needed by the user).” | limitations[0] |
| limitation | There are typically far more candidate biomarkers than samples, which is a property of the data and not of the method— “Related challenges are associated w ith high-dimensional data regardless of the data type or experimental platform; the number of potential biomarkers, for example, is typically much higher than the number of samples” | limitations[1] |
| limitation | Microbial communities show remarkably high inter-subject variability, alongside sequencing errors and chimeric reads— “Metagenomic analyses additionally present their own specific issues, including sequencing errors, chimeric reads [27,28], and complex underlying biology; many microbial communities have been found to show remarkably high inter-subject variability.” | limitations[2] |
| limitation | Finding a biomarker is stated to be only a PRECURSOR to understanding a mechanism - the method does not claim to deliver the biology— “Moreover, simply identifying potential biomarkers without elucidating their biological consistency and roles is only a precursor to understanding the underlying mechanisms of microbe-microbe or host-microbe interactions” | limitations[3] |
| concept | class comparison— “This problem is referred to as class comparison: how can the differences between phenotypes such as tumor subtype or disease state be explained in terms of consistent biological pathways or molecular mechanisms?” | concepts[0] |
| concept | class discovery— “Comparisons between healthy and diseased tissues have highlighted the importance of tasks such as class discovery (detecting novel subtypes of a disease) and class prediction (determining the subtype of a new sample)” | concepts[1] |
| concept | class prediction— “Comparisons between healthy and diseased tissues have highlighted the importance of tasks such as class discovery (detecting novel subtypes of a disease) and class prediction (determining the subtype of a new sample)” | concepts[2] |
| concept | LEfSe— “LEfSe determines the features (organisms, clades, operational taxonomic units, genes, or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance.” | concepts[3] |
| concept | effect size— “In this work, we present the linear discriminant analysis (LDA) effect size (LEfSe) method to support highdimensional class comparisons with a particular focus on metagenomic analyses. LEfSe… | concepts[4] |
| concept | biological consistency | concepts[5] |
| concept | effect relevance Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[6] |
| concept | statistical significance Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[7] |
| concept | biomarkers Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[8] |
| concept | false positive rate Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[9] |
| concept | false negative rate Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[10] |
| concept | P-value Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[11] |
| concept | inter-subject variability Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[12] |
| concept | Wilcoxon rank-sum test Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[13] |
| concept | prior biological knowledge Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[14] |
| concept | high-dimensional data Quotation withheld — this paper is not ours to reproduce, and the page has already quoted as much of it as the rights layer allows. The tag, its field and its confidence stand; the sentence is at the publisher. | concepts[15] |
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