Evidence map›Paper›PMID 36712131›Full record

ArticlebioRxiv : the preprint server for biology2023

Bayesian Differential Analysis of Cell Type Proportions.

Tanya T Karagiannis, Stefano Monti, Paola Sebastiani

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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

5 · Who and what money

Authors and funding

3 authors.

Tanya T KaragiannisInstitute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
Stefano MontiDivision of Computational Biomedicine, Boston University School of Medicine, Boston, MA, USA.
Paola SebastianiInstitute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.

Funding

Identifying protective omics profiles in centenarians and translating these into preventive and therapeutic strategiesUH2AG064704 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI PERLS, THOMAS T, SEBASTIANI, PAOLA · 2019 to 2021
$9.4M
Integrative Omics to enhance therapeutics development for healthy agingUH3AG064706 · NIA · TRANSLATIONAL GENOMICS RESEARCH INST · PI MILLER, RICHARD A, SCHORK, NICHOLAS JOSEPH · 2021 to 2024
$3.3M
NIA NIH HHS UH2 AG064704NIA NIH HHS UH3 AG064706
6 · The paper itself

Abstract

The analysis of cell type proportions in a biological sample should account for the compositional nature of the data but most analyses ignore this characteristic with the risk of producing misleading conclusions. The recent method scCODA appropriately incorporates these constraints by using a Bayesian Multinomial-Dirichlet model that requires a reference cell type to normalize the distribution of all cell types. However, a reference cell type that is stable across biological conditions may not always be available. Here, we present an approach that uses a Bayesian multinomial regression for the analysis of single cell distribution data without the need for a reference cell type. We show an implementation example using the rjags package within the R software.

Identifiers

PMID36712131
PMCPMC9882175

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.