Fig. 4

¹⁶O analysis of self-organizing maps for each time point. a,b,c,d,e Kohonen Self-Organizing Map (SOM) was applied to the differentially expressed (DE) transcripts obtained from the RNA-Seq data to identify coherent patterns of transcript expression at each time point, as well as patterns within the unannotated transcripts. The SOM clusters transcripts in each module according to log₂(fold change) of the expression values. SOM clustering analysis demonstrates the distances between correlated transcript groups. The small blue hexagons are modules comprising transcripts with similar log₂(fold change) expression patterns. The numbers of transcripts in each module are provided in Supplemental Fig. 2. Neighboring modules are connected with a red line. The colors of the lines connecting the modules indicate the similarity between modules: Lighter colors represent higher similarity, and darker colors represent lower similarity. f Expression patterns of unannotated transcripts were identified, and the corresponding modules (represented in circled numbers) were further analyzed by IPA. Only the most significant pathways across all clusters are shown with available color-coded activation z-scores. Inhibitory, activation, or unknown directionality z-scores corresponds to green, red, and white respectively. The entries with white color indicate the directionality could not be predicted based on the available data, yet the pathway is significantly identified by pathway analysis. The goal of the IPA downstream effects analysis is to identify functional pathways whose activity is expected to be increased or decreased, given the observed expression changes in a user’s dataset (see Methods)