Figure 2 from the paper
Transcriptional trajectories of human kidney injury progression
Figure 2 from the paper
Transcriptional trajectories of human kidney injury progression
Abstract
We investigated a complex clinical condition applying an unsupervised computational strategy, which integrates genome-wide expression analysis in heterogeneous groups of patients to identify and characterize shared trajectories of disease progression. The integration of multiple transcriptomes from serial biopsies with advanced computational algorithms overcame the analytical hurdles related to variability between individuals and identified shared transcriptional elements of kidney disease progression in humans, which may prove as useful predictors of disease progression following kidney transplantation and kidney injury. This generally applicable approach opens the way for an unbiased analysis of human disease progression.
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In this research, I applied a Linear Mixed Model to achieve variance decomposition, which is pretty interesting. It helps us distinguish between genes that are explained more by confounding variables like sex, immune reponse, from the real signals that are explained by injury stage.