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71ce29d8-7b35-4239-932b-13346d3dea8c - samples

ABSTRACT: To model recovery dynamics, using severe COVID-19 as the example, we align heterogeneous recovery trajectories via a novel computational scheme applied to longitudinally sampled blood transcriptomes. We thus generate pseudotime trajectories, which we then link to cellular and molecular mechanisms based on cell deconvolution analysis and molecular pathway prediction, thus presenting a unique framework for studying recovery processes over time.

PROVIDER: EGAD00001008331 | EGA |

REPOSITORIES: EGA

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