Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19.

ABSTRACT: Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there are not enough methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic data sets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy, to identify and visualize distinct dynamic transcriptional gene signatu

SUBMITTER: Wang X 

PROVIDER: S-EPMC9057597 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature