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Representing and extracting knowledge from single-cell data
Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR). Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS).ORCID iD: 0000-0002-9322-5879
Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR). Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS).ORCID iD: 0000-0003-2475-3528
Umeå University, Faculty of Medicine, Umeå Centre for Microbial Research (UCMR). Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS).ORCID iD: 0000-0002-7745-2844
2023 (English)In: Biophysical Reviews, ISSN 1867-2450Article, review/survey (Refereed) Published
Abstract [en]

Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.

Place, publisher, year, edition, pages
2023.
Keywords [en]
Generating processes, Graphs, Graphs, Markov chains, Neural networks, NLP, Single-cell, Statistics, Topology, VAE
National Category
Cell and Molecular Biology
Identifiers
URN: urn:nbn:se:umu:diva-212821DOI: 10.1007/s12551-023-01091-4ISI: 001060089100001Scopus ID: 2-s2.0-85166985324OAI: oai:DiVA.org:umu-212821DiVA, id: diva2:1788602
Funder
Swedish Research Council, 2021-06602Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2024-05-07Bibliographically approved

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Mihai, Ionut SebastianHenriksson, Johan

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Mihai, Ionut SebastianChafle, SarangHenriksson, Johan
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Umeå Centre for Microbial Research (UCMR)Department of Molecular Biology (Faculty of Medicine)Molecular Infection Medicine Sweden (MIMS)
Cell and Molecular Biology

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