ArticlebioRxiv : the preprint server for biology2025
GUIDING CLUSTERING AND ANNOTATION IN SINGLE-CELL RNA SEQUENCING USING THE AVERAGE OVERLAP METRIC.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
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Authors and funding
4 authors.
Funding
Abstract
Defining cell types using unsupervised clustering algorithms based on transcriptional similarity is a powerful application of single-cell RNA sequencing. A single clustering resolution may not yield clusters that represent both broad, well-defined populations and smaller subpopulations simultaneously. Therefore, when cell identities are not known prior to sequencing, robust comparison and annotation of inferred
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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.