Journal article Open Access

Benchmarking of cell type deconvolution pipelines for transcriptomics data

Cobos, F.; Alquicira-Hernandez, J.; Powell, J.; Mestdagh, P.; Peter, K.


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  <identifier identifierType="URL">https://zenodo.org/record/4312852</identifier>
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      <creatorName>Cobos, F.</creatorName>
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      <affiliation>Ghent University</affiliation>
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    <creator>
      <creatorName>Alquicira-Hernandez, J.</creatorName>
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      <familyName>Alquicira-Hernandez</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9049-7780</nameIdentifier>
      <affiliation>Garvan Institute of Medical Research</affiliation>
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    <creator>
      <creatorName>Powell, J.</creatorName>
      <givenName>J.</givenName>
      <familyName>Powell</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5070-4124</nameIdentifier>
      <affiliation>Garvan Institute of Medical Research</affiliation>
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    <creator>
      <creatorName>Mestdagh, P.</creatorName>
      <givenName>P.</givenName>
      <familyName>Mestdagh</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7821-9684</nameIdentifier>
      <affiliation>Cancer Research Institute Ghent (CRIG)</affiliation>
    </creator>
    <creator>
      <creatorName>Peter, K.</creatorName>
      <givenName>K.</givenName>
      <familyName>Peter</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7726-5096</nameIdentifier>
      <affiliation>Ghent University</affiliation>
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  <titles>
    <title>Benchmarking of cell type deconvolution pipelines for transcriptomics data</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-12-02</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4312852</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1038/s41467-020-20288-9</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ipc</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, preprocessing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semisupervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/826121/">826121</awardNumber>
      <awardTitle>individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology</awardTitle>
    </fundingReference>
  </fundingReferences>
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