Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases

Autor: Francesco Vallania, Andrew Tam, Shane Lofgren, Steven Schaffert, Tej D. Azad, Erika Bongen, Winston Haynes, Meia Alsup, Michael Alonso, Mark Davis, Edgar Engleman, Purvesh Khatri
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Nature Communications, Vol 9, Iss 1, Pp 1-8 (2018)
Druh dokumentu: article
ISSN: 2041-1723
DOI: 10.1038/s41467-018-07242-6
Popis: Cell type deconvolution from bulk expression data rely on a reference expression matrix. Here, the authors introduce a basis matrix built using data from both healthy and diseased samples profiled on 42 platforms, reducing biases introduced by single-platform matrices built using healthy samples.
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