ArticleCytometry. Part A : the journal of the International Society for Analytical Cytology2022
Automated identification of maximal differential cell populations in flow cytometry data.
Article in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- A cell-level discriminative neural network model for diagnosis of blood cancers.Bioinformatics (Oxford, England) · 2023Article
- Automated identification of maximal differential cell populations in flow cytometry data.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2022Article
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Authors and funding
4 authors.
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Abstract
We introduce a new cell population score called SpecEnr (specific enrichment) and describe a method that discovers robust and accurate candidate biomarkers from flow cytometry data. Our approach identifies a new class of candidate biomarkers we define as driver cell populations, whose abundance is associated with a sample class (e.g., disease), but not as a result of a change in a related population. We show that the driver cell populations we find are also easily interpretable using a lattice-based visualization tool. Our method is implemented in the R package flowGraph, freely available on GitHub (github.com/aya49/flowGraph) and on BioConductor.
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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.