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  Visualizing Cluster-specific Genes from Single-cell Transcriptomics Data Using Association Plots

Gralinska, E., Kohl, C., Fadakar, S., & Vingron, M. (2022). Visualizing Cluster-specific Genes from Single-cell Transcriptomics Data Using Association Plots. Journal of Molecular Biology, 167525. doi:10.1016/j.jmb.2022.167525.

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JMolBiol_Gralinska et al_2022.pdf (Publisher version), 9MB
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 Creators:
Gralinska, Elzbieta1, Author              
Kohl, Clemens1, Author              
Fadakar, Sokhandan2, Author              
Vingron, Martin1, Author              
Affiliations:
1Transcriptional Regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              
2Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              

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 Abstract: Visualizing single-cell transcriptomics data in an informative way is a major challenge in biological data analysis. Clustering of cells is a prominent analysis step and the results are usually visualized in a planar embedding of the cells using methods like PCA, t-SNE, or UMAP. Given a cluster of cells, one frequently searches for the genes highly expressed specifically in that cluster. At this point, visualization is usually replaced by studying a list of differentially expressed genes. Association Plots are derived from correspondence analysis and constitute a planar visualization of the features which characterize a given cluster of observations. We have adapted Association Plots to address the challenge of visualizing cluster-specific genes in large single-cell data sets. Our method is made available as a free R package called APL. We demonstrate the application of APL and Association Plots to single-cell RNA-seq data on two example data sets. First, we present how to delineate novel marker genes using Association Plots with the example of Peripheral Blood Mononuclear Cell data. Second, we show how to apply Association Plots for annotating cell clusters to known cell types using Association Plots and a predefined list of marker genes. To do this we will use data from the human cell atlas of fetal gene expression. Results from Association Plots will also be compared to methods for deriving differentially expressed genes, and we will show the integration of APL with Gene Ontology Enrichment.

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Language(s): eng - English
 Dates: 2022-02-282022-03-07
 Publication Status: Published online
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1016/j.jmb.2022.167525
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Title: Journal of Molecular Biology
  Other : JMB
  Abbreviation : J. Mol. Biol.
Source Genre: Journal
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Publ. Info: Elsevier
Pages: - Volume / Issue: - Sequence Number: 167525 Start / End Page: - Identifier: ISSN: 0022-2836
CoNE: https://pure.mpg.de/cone/journals/resource/0022-2836