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Cluster-independent marker feature identification from single-cell omics data using SEMITONES

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Item Type:Article
Title:Cluster-independent marker feature identification from single-cell omics data using SEMITONES
Creators Name:Vlot, A.H.C. and Maghsudi, S. and Ohler, U.
Abstract:Identification of cell identity markers is an essential step in single-cell omics data analysis. Current marker identification strategies typically rely on cluster assignments of cells. However, cluster assignment, particularly for developmental data, is nontrivial, potentially arbitrary, and commonly relies on prior knowledge. In response, we present SEMITONES, a principled method for cluster-free marker identification. We showcase and evaluate its application for marker gene and regulatory region identification from single-cell data of the human haematopoietic system. Additionally, we illustrate its application to spatial transcriptomics data and show how SEMITONES can be used for the annotation of cells given known marker genes. Using several simulated and curated data sets, we demonstrate that SEMITONES qualitatively and quantitatively outperforms existing methods for the retrieval of cell identity markers from single-cell omics data.
Source:Nucleic Acids Research
ISSN:0305-1048
Publisher:Oxford University Press
Page Range:gkac639
Date:31 July 2022
Official Publication:https://doi.org/10.1093/nar/gkac639
PubMed:View item in PubMed

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