ArticleIEEE computer graphics and applications
The State of Single-Cell Atlas Data Visualization in the Biological Literature.
Article in IEEE computer graphics and applications. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Recent advancements have enabled tissue samples to be profiled at the unprecedented level of detail of a single cell. Analysis of these data has enabled discoveries that are relevant to understanding disease and developing therapeutics. Large-scale profiling efforts are underway, which aim to generate "atlas" resources that catalog cellular archetypes, including biomarkers and spatial locations. While the problem of cellular data visualization is not new, the size, resolution, and heterogeneity of single-cell atlas datasets present challenges and opportunities. We survey the usage of visualization to interpret single-cell atlas datasets by assessing over 1800 figure panels from 45 biological publications. We intend for this report to serve as a foundational resource for the visualization community as atlas-scale single-cell datasets are emerging rapidly with aims of advancing our understanding of biological function in health and disease.
Indexed as
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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.