Evidence map›Paper›PMID 42699185›Full record

ArticleNAR genomics and bioinformatics2026

Proportionality-based association metrics in count compositional data.

Kevin McGregor, Nneka Okaeme, Reihane Khorasaniha, Simona Veniamin, Juan Jovel, Richard Miller, Ramsha Mahmood, Morag Graham, Christine Bonner, Charles N Bernstein and 14 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NAR genomics and bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

24 authors.

Kevin McGregorDepartment of Statistics, University of Manitoba, Manitoba, R3T 2M5,  Canada.ORCID https://orcid.org/0000-0002-0957-5009
Nneka OkaemeDepartment of Statistics and Actuarial Science, University of Waterloo, Ontario, N2L 3G1,  Canada.
Reihane KhorasanihaDepartment of Food and Human Nutritional Sciences, University of Manitoba, Manitoba, R3T 2N2,  Canada.
Simona VeniaminDepartment of Medicine, Division of Gastroenterology, University of Alberta, Alberta, T6G 2B7,  Canada.
Juan JovelFaculty of Veterinary Medicine, University of Calgary, Alberta, T2N 1N4, Canada.
Richard MillerDepartment of Internal Medicine, University of Manitoba, Manitoba, R3A 1R9, Canada.
Ramsha MahmoodDepartment of Internal Medicine, University of Manitoba, Manitoba, R3A 1R9, Canada.
Morag GrahamNational Microbiology Laboratory, Public Health Agency of Canada, Manitoba, R3E 3M4,  Canada.
Christine BonnerNational Microbiology Laboratory, Public Health Agency of Canada, Manitoba, R3E 3M4,  Canada.
Charles N BernsteinDepartment of Internal Medicine, University of Manitoba, Manitoba, R3A 1R9, Canada.
Douglas L ArnoldDepartment of Neurology and Neurosurgery, McGill University, Québec, H3A 1A1,  Canada.
Amit Bar-OrCenter for NeuroInflammation and Experimental Therapeutics and Children's Hospital of Philadelphia, University of Pennsylvania, Pennsylvania, 19104,  United States.
Ruth Ann MarrieDepartments of Medicine and Community Health and Epidemiology, Dalhousie University, Nova Scotia, B3H 1V7,  Canada.ORCID https://orcid.org/0000-0002-1855-5595
Julia O'MahonyMellen Center for Multiple Sclerosis, Cleveland Clinic, Ohio, 44195,  United States.
Eluen Ann YehDivision of Neurology, Department of Pediatrics, Hospital for Sick Children, University of Toronto, Ontario, M5G 1X8,  Canada.
Yinshan ZhaoDepartment of Medicine, University of British Columbia, British Columbia, V6T 2B5,  Canada.
Brenda BanwellDepartment of Pediatrics, Johns Hopkins University, Maryland, 21287,  United States.
Emmanuelle WaubantWeill Institute for Neurosciences, University of California San Francisco, California, 94158,  United States.
Natalie KnoxNational Microbiology Laboratory, Public Health Agency of Canada, Manitoba, R3E 3M4,  Canada.
Gary Van DomselaarNational Microbiology Laboratory, Public Health Agency of Canada, Manitoba, R3E 3M4,  Canada.
Feng ZhuDepartment of Medicine, University of British Columbia, British Columbia, V6T 2B5,  Canada.
Ali I MirzaDepartment of Medicine, University of British Columbia, British Columbia, V6T 2B5,  Canada.
Helen TremlettDepartment of Medicine, University of British Columbia, British Columbia, V6T 2B5,  Canada.ORCID https://orcid.org/0000-0001-5804-2535
Heather ArmstrongDepartment of Medicine, Division of Gastroenterology, University of Alberta, Alberta, T6G 2B7,  Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compositional data comprise vectors that describe the constituent parts of a whole. Data arising from various -omics platforms such as 16S and RNA sequencing are compositional in nature. In this kind of data, correlations between features on raw counts have no meaningful interpretation. Metrics of proportionality were formulated to address this problem. However, an inherent bias arises when these metrics are calculated empirically on count-based measures due to variability in read depths. We quantify the bias introduced by empirically calculating proportionality-based association metrics in count data. Additionally, we propose a means of estimating these metrics within a logit-normal multinomial model in pursuit of more accurate estimates. The model-based estimates are shown to outperform empirical estimates in simulated data and are applied to a mouse embryonic stem cell single-cell sequencing dataset, as well as a pediatric-onset multiple sclerosis metagenomic dataset.

Indexed as

MetagenomicsAlgorithmsAnimalsMiceSequence Analysis, RNASingle-Cell Analysis

Identifiers

PMID42699185
PMCPMC13542730

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.