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Adaptive digital tissue deconvolution.
Görtler, Franziska; Mensching-Buhr, Malte; Skaar, Ørjan; Schrod, Stefan; Sterr, Thomas; Schäfer, Andreas; Beißbarth, Tim; Joshi, Anagha; Zacharias, Helena U; Grellscheid, Sushma Nagaraja; Altenbuchinger, Michael.
Afiliación
  • Görtler F; Computational Biology Unit, Department of Biological Sciences, University of Bergen, N-5008 Bergen, Norway.
  • Mensching-Buhr M; Department of Oncology and Medical Physics, Haukeland University Hospital, 5021 Bergen, Norway.
  • Skaar Ø; Department of Medical Bioinformatics, University Medical Center Göttingen, 37075 Göttingen, Germany.
  • Schrod S; Department of Informatics, Computational Biology Unit, University of Bergen, N-5008 Bergen, Norway.
  • Sterr T; Department of Medical Bioinformatics, University Medical Center Göttingen, 37075 Göttingen, Germany.
  • Schäfer A; Institute of Theoretical Physics, University of Regensburg, 93053 Regensburg, Germany.
  • Beißbarth T; Institute of Theoretical Physics, University of Regensburg, 93053 Regensburg, Germany.
  • Joshi A; Department of Medical Bioinformatics, University Medical Center Göttingen, 37075 Göttingen, Germany.
  • Zacharias HU; Department of Clinical Science, Computational Biology Unit, University of Bergen, N-5008 Bergen, Norway.
  • Grellscheid SN; Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, 30625 Hannover, Germany.
  • Altenbuchinger M; Computational Biology Unit, Department of Biological Sciences, University of Bergen, N-5008 Bergen, Norway.
Bioinformatics ; 40(Suppl 1): i100-i109, 2024 06 28.
Article en En | MEDLINE | ID: mdl-38940181
ABSTRACT
MOTIVATION The inference of cellular compositions from bulk and spatial transcriptomics data increasingly complements data analyses. Multiple computational approaches were suggested and recently, machine learning techniques were developed to systematically improve estimates. Such approaches allow to infer additional, less abundant cell types. However, they rely on training data which do not capture the full biological diversity encountered in transcriptomics analyses; data can contain cellular contributions not seen in the training data and as such, analyses can be biased or blurred. Thus, computational approaches have to deal with unknown, hidden contributions. Moreover, most methods are based on cellular archetypes which serve as a reference; e.g. a generic T-cell profile is used to infer the proportion of T-cells. It is well known that cells adapt their molecular phenotype to the environment and that pre-specified cell archetypes can distort the inference of cellular compositions.

RESULTS:

We propose Adaptive Digital Tissue Deconvolution (ADTD) to estimate cellular proportions of pre-selected cell types together with possibly unknown and hidden background contributions. Moreover, ADTD adapts prototypic reference profiles to the molecular environment of the cells, which further resolves cell-type specific gene regulation from bulk transcriptomics data. We verify this in simulation studies and demonstrate that ADTD improves existing approaches in estimating cellular compositions. In an application to bulk transcriptomics data from breast cancer patients, we demonstrate that ADTD provides insights into cell-type specific molecular differences between breast cancer subtypes. AVAILABILITY AND IMPLEMENTATION A python implementation of ADTD and a tutorial are available at Gitlab and zenodo (doi10.5281/zenodo.7548362).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático Límite: Female / Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Automático Límite: Female / Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Noruega Pais de publicación: Reino Unido