ArticleeLife2017
Decoupling global biases and local interactions between cell biological variables.
Article in eLife, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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.
Who cites it
9 citing papers in PubMed.
- MultiMatch: geometry-informed colocalization in multi-color super-resolution microscopy.Communications biology · 2024Article
- Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis.Cell reports methods · 2022Review
- Colocalization for super-resolution microscopy via optimal transport.Nature computational science · 2021Article
- Bioimage Analysis and Cell Motility.Patterns (New York, N.Y.) · 2021Review
- Community standards for open cell migration data.GigaScience · 2020Article
- Temporal Ordering in Endocytic Clathrin-Coated Vesicle Formation via AP2 Phosphorylation.Developmental cell · 2019Article
- Sharing and reusing cell image data.Molecular biology of the cell · 2018Article
- Mapping molecular assemblies with fluorescence microscopy and object-based spatial statistics.Nature communications · 2018Article
- Multiscale Characterization of Engineered Cardiac Tissue Architecture.Journal of biomechanical engineering · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Analysis of coupled variables is a core concept of cell biological inference, with co-localization of two molecules as a proxy for protein interaction being a ubiquitous example. However, external effectors may influence the observed co-localization independently from the local interaction of two proteins. Such global bias, although biologically meaningful, is often neglected when interpreting co-localization. Here, we describe DeBias, a computational method to quantify and decouple global bias from local interactions between variables by modeling the observed co-localization as the cumulative contribution of a global and a local component. We showcase four applications of DeBias in different areas of cell biology, and demonstrate that the global bias encapsulates fundamental mechanistic insight into cellular behavior. The DeBias software package is freely accessible online via a web-server at https://debias.biohpc.swmed.edu.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.