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Cellular deconvolution with continuous transitions
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DOI：10.1038/s43588-023-00489-0.png)
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Abstract
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A recent work introduces a cellular deconvolution method, MeDuSA, of estimating cell-state abundance along a one-dimensional trajectory from bulk RNA-seq data with fine resolution and high accuracy, enabling the characterization of cell-state transition in various biological processes.
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The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.png)
NATURE BIOTECHNOLOGY
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NATURE BIOTECHNOLOGYIF41.7
OAAIShareSaveMixed model-based deconvolution of cell-state abundances (MeDuSA) along a one-dimensional trajectoryOAAI
OAAIShareSaveA benchmark for RNA-seq deconvolution analysis under dynamic testing environmentsGENOME BIOLOGYOAAI
GENOME BIOLOGYIF9.4
OAAIShareSaveDetermining cell type abundance and expression from bulk tissues with digital cytometryNATURE BIOTECHNOLOGYOAAI
NATURE BIOTECHNOLOGYIF41.7
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