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Differential abundance analysis for microbial marker-gene surveys
Open accessAbstract
We introduce a methodology to assess differential abundance in sparse high-throughput microbial marker-gene survey data. Our approach, implemented in the metagenomeSeq Bioconductor package, relies on a novel normalization technique and a statistical model that accounts for undersampling—a common feature of large-scale marker-gene studies. Using simulated data and several published microbiota data sets, we show that metagenomeSeq outperforms the tools currently used in this field.
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Computational methods and challenges in analyzing intratumoral microbiome data
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The bacterial communities of the small intestine and stool in children with short bowel syndrome
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“For further statistical analysis, the sequence dataset was normalized by cumulative sum scaling to minimize the effect of different sequence numbers obtained with each sample (Paulson et al., 2013).”
MethodMulticytokine-producing CD4+ T cells characterize the livers of patients with NASH
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Lactic microbiota of the minas artisanal cheese produced in the serro region, Minas Gerais, Brazil
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Metadata
- Authors
- Paulson, J. N., Stine, O. C., Bravo, H. C., & Pop, M.
- Venue
- Nature Methods
- Published
- 2013-09-29
- Volume
- 10
- Issue
- 12
- Pages
- 1200-1202
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2026006003
- Cited by
- 2,428
- References
- 9
- FWCI
- 23.67
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.