Partitioned Local Depth analysis of time course transcriptomic data reveals elaborate community structure

Abstract

Transcriptome studies that provide temporal information are valuable for identifying groups of similarly-behaving transcripts, giving insight into overarching gene regulatory networks. Nevertheless, inferring transcriptional networks from time series data is challenging, in part because it is difficult to holistically consider both local relationships and global structure of these complex and overlapping transcriptional responses. To address this need, we employed the Partitioned Local Depth (PaLD) method to examine four time series transcriptomic datasets generated using the model plant Arabidopsis thaliana. Here, we provide a self-contained description of the method and demonstrate how it can be used to make predictions about gene regulatory networks based on time series data. The analysis provides a global network representation of the data from which graph partitioning methods and neighborhood analysis can reveal smaller, more well-defined groups of like-responding transcripts. These groups of transcripts that change in response to hormone treatment (e.g., auxin or ethylene) or high salinity were demonstrated to be enriched in common biological function and/or binding of transcription factors that were not identified with prior analyses of this data using other clustering and inference methodologies. These results reveal the ability of PaLD to generate predictions about gene regulatory networks using time series transcriptomic data, which can be of value to the systems biology community.