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A multiresolution framework to characterize single-cell state landscapes

Author

Listed:
  • Shahin Mohammadi

    (MIT Computer Science and Artificial Intelligence Laboratory
    Broad Institute of MIT and Harvard)

  • Jose Davila-Velderrain

    (MIT Computer Science and Artificial Intelligence Laboratory
    Broad Institute of MIT and Harvard)

  • Manolis Kellis

    (MIT Computer Science and Artificial Intelligence Laboratory
    Broad Institute of MIT and Harvard)

Abstract

Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although many methods and approaches exist, identifying cell states and their underlying topology is still a major challenge. Here, we introduce the concept of multiresolution cell-state decomposition as a practical approach to simultaneously capture both fine- and coarse-grain patterns of variability. We implement this concept in ACTIONet, a comprehensive framework that combines archetypal analysis and manifold learning to provide a ready-to-use analytical approach for multiresolution single-cell state characterization. ACTIONet provides a robust, reproducible, and highly interpretable single-cell analysis platform that couples dominant pattern discovery with a corresponding structural representation of the cell state landscape. Using multiple synthetic and real data sets, we demonstrate ACTIONet’s superior performance relative to existing alternatives. We use ACTIONet to integrate and annotate cells across three human cortex data sets. Through integrative comparative analysis, we define a consensus vocabulary and a consistent set of gene signatures discriminating against the transcriptomic cell types and subtypes of the human prefrontal cortex.

Suggested Citation

  • Shahin Mohammadi & Jose Davila-Velderrain & Manolis Kellis, 2020. "A multiresolution framework to characterize single-cell state landscapes," Nature Communications, Nature, vol. 11(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18416-6
    DOI: 10.1038/s41467-020-18416-6
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    Cited by:

    1. Ayano Matsushima & Sergio Sebastian Pineda & Jill R. Crittenden & Hyeseung Lee & Kyriakitsa Galani & Julio Mantero & Geoffrey Tombaugh & Manolis Kellis & Myriam Heiman & Ann M. Graybiel, 2023. "Transcriptional vulnerabilities of striatal neurons in human and rodent models of Huntington’s disease," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Nelson Johansen & Hongru Hu & Gerald Quon, 2023. "Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    3. Hyun Kim & Won Chang & Seok Joo Chae & Jong-Eun Park & Minseok Seo & Jae Kyoung Kim, 2024. "scLENS: data-driven signal detection for unbiased scRNA-seq data analysis," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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