The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Proportionality: A Valid Alternative to Correlation for Relative Data
Open accessAbstract
In the life sciences, many measurement methods yield only the relative abundances of different components in a sample. With such relative-or compositional-data, differential expression needs careful interpretation, and correlation-a statistical workhorse for analyzing pairwise relationships-is an inappropriate measure of association. Using yeast gene expression data we show how correlation can be misleading and present proportionality as a valid alternative for relative data. We show how the strength of proportionality between two variables can be meaningfully and interpretably described by a new statistic ϕ which can be used instead of correlation as the basis of familiar analyses and visualisation methods, including co-expression networks and clustered heatmaps. While the main aim of this study is to present proportionality as a means to analyse relative data, it also raises intriguing questions about the molecular mechanisms underlying the proportional regulation of a range of yeast genes.
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Cites
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A General Framework for Weighted Gene Co-Expression Network Analysis
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Aitchison, J. (1986) · doi:10.1007/978-94-009-4109-0 · Cited by 4,383
Microbial co-occurrence relationships in the human microbiome
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“The largest cluster of proportionally regulated mRNAs (96 genes, S1 Supporting Information) was highly enriched for mRNAs down-regulated as part of the core environmental stress response [19], including 66 mRNAs that encode ribosomal proteins, and the remaining mRNAs also associated with roles in protein translation,…”
BackgroundSelf-citationRevisiting Global Gene Expression Analysis
Lovén, J., Orlando, D. A., Sigova, A. A., Lin, C. Y., Rahl, P. B., Burge, C. B., Levens, D. L., Lee, T. I., & Young, R. A. (2012) · Cell · doi:10.1016/j.cell.2012.10.012 · Cited by 616
Cited by
Microbiome Datasets Are Compositional: And This Is Not Optional
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V., & Egozcue, J. J. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02224 · Cited by 3,044
“…have been incorporated into the R package propr, that includes an adaptation allowing the calculation of the metrics with sparse data that gives an expected value of ρ (E(ρ)), that approaches 1 if the two features have exactly constant ratios in the data (Lovell et al., 2015; Quinn et al., 2017).”
BackgroundMethodInfluentialSelf-citationBest practices for analysing microbiomes
Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., et al. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0029-9 · Cited by 2,030
Recovery of gut microbiota of healthy adults following antibiotic exposure
Palleja, A., Mikkelsen, K. H., Forslund, S. K., Kashani, A., Allin, K. H., Nielsen, T., Hansen, T. H., Liang, S., Feng, Q., Zhang, C., et al. (2018) · Nature Microbiology · doi:10.1038/s41564-018-0257-9 · Cited by 875
Interpreting prevotella and bacteroides as biomarkers of diet and lifestyle
Gorvitovskaia, A., Holmes, S. P., & Huse, S. M. (2016) · Microbiome · doi:10.1186/s40168-016-0160-7 · Cited by 461
Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data
Gloor, G. B., & Reid, G. (2016) · Canadian Journal of Microbiology · doi:10.1139/cjm-2015-0821 · Cited by 373
Metadata
- Authors
- Lovell, D., Pawlowsky-Glahn, V., Egozcue, J. J., Marguerat, S., & Bähler, J.
- Venue
- PLOS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-03-16
- Volume
- 11
- Issue
- 3
- Pages
- e1004075
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- OpenAlex
- W2112408821
- Cited by
- 281
- References
- 6
- FWCI
- 9.26
- Type
- journal article
- Language
- en
- Open access
- gold
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- Lovell, D., Pawlowsky-Glahn, V., Egozcue, J. J., Marguerat, S., & Bähler, J. (2015). Proportionality: A Valid Alternative to Correlation for Relative Data. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1004075
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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